Mohamed Ben-Marzouk
email: mohamed.ben-marzouk@univ-lyon1.fr

Guy Clerc

Serge Pelissier

Ali Sari

Pascal Venet

Determination of the electric vehicles driving modes in real life conditions by classification methods

Keywords: Electric vehicle, life-size experimentation, driving modes, modes of use, classification, batteries I

In order to study the aging of batteries in automotive applications, it is important to understand how and under what conditions these batteries are operating in electric vehicles (EVs).

However, because of the specificities of EVs, these uses may be very different from those known from internal combustion engine cars (ICE).

In this paper, we present how, from real-life data, we determine the different driving modes of electric vehicles. The paper presents the different techniques adopted to analyze and classify the data and the different EV running modes, which are obtained.

INTRODUCTION

Our planet is more and more suffering from the effects of pollution. During the last decades, it has reached records, and takes all forms: pollution of soil, water, air, etc. All of these increase the health and environmental risks.

Air pollution is the most difficult to control. It is caused mainly by agriculture, industrial waste, homes and transportation. In the context of this air pollution, the most difficult source to manage is transportation because it is a source of mobile pollution. In fact, according to CITEPA in France, the transport sector is responsible for the emission of nearly 15% of PM10 (particles with a diameter of less than 10 micrometers) and more than 50% of carbon soot [START_REF] Citepa | Poussières en suspension -CITEPA[END_REF]. Thanks to people's awareness of environmental risks and the threats of fossil fuel depletion, the electric vehicle (EV) is now booming with a number of registrations that are constantly increasing (up 23 % between 2015 and 2016) [START_REF] Avere-France | Véhicules électriques immatriculés en 2016[END_REF].

Despite the policy of countries to reduce pollution in cities by encouraging the purchase of EVs, they remain nonetheless widespread. This is due to the limitations of electric vehicles that are mainly related to batteries problems. It is indeed their weak autonomy, their relatively high prices (400 € / kWh) which can reach 40% of the value of the car and their limited lifetime.

The problem of autonomy seems to be in the process of being resolved since the new models of EVs display a much greater autonomy than that of the models of a few years ago. The high price of batteries can be mitigated by massive production. However, the problem of the lifetime is still relevant. Indeed, mastering the lifespan of storage systems can save on the cost of replacement at the end of life and also on the right energy dimensioning of the system.

There are several research projects around the world dealing with the aging of lithium batteries. However, most of these works only take into account calendar aging or cycling aging.

Yet, in real-life automotive application, lithium batteries are facing an alternation between these types of aging.

Currently, lifetime predictions are based on models and results from standardized cycling tests, with accelerated aging profiles consisting of partial or total charge / discharge, constant currents, or simplified profiles inspired by real uses but which consist of simple impulses and only partially represent the diversity of uses.

To study the problems related to battery lifetime in automotive applications, many authors count on simulations with standardized cycles such as NEDC (New European Driving Cycle) and WLTC (Worldwide Harmonized Light Vehicle Test Cycles) [START_REF] Peterson | Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization[END_REF], [START_REF] Wood | Investigation of battery endof-life conditions for plug-in hybrid electric vehicles[END_REF]. The results found are often empirical and cannot be very precise. Electric vehicles and their operation depend on many specific factors and parameters. They depend, among other things, on as temperature, SoC, etc.

It is for these reasons that it will be more valuable to seek the different EVs modes of use, starting from real life batteries data recording and their operating conditions, in order to determine their effects on aging later.

In this paper, we will present the methodology followed to search for the different electric vehicles modes of use. We will start with a presentation of the used database. Then we explain the different steps performed to obtain the modes of use.

II. DATABASE DESCRIPTION

This study is based on data extracted from ten EVs. These data are provided by the LTE-IFSTTAR laboratory and come from the CROME project (CROss-border Mobility for EVs). On this project, several French and German partners worked to design, realize and analyze an electric mobility system between France and Germany [START_REF] Kreczanik | Construction of Database on Real World Uses of Electric Vehicles -A French Case[END_REF]. This project has enabled LTE-IFSTTAR to acquire a very large database of EVs in real life application.

In fact, these EVs, having the same architecture and belonging to private and professional volunteers, have been equipped with data loggers that record and transmit via GSM a large number (more than 500) of variables related to the operation (speed, brake use, acceleration rate, etc.), data related to the batteries (current, voltage, cell temperature, etc.) and other related to the engine (torque, engine temperature, etc.).

For each vehicle, the follow-up during a couple of years has allowed to collect thousands of files. Each file represents one use of the EV, in running or in charging conditions. In this file, we find a follow-up of the evolutions of all the variables related to EV over time with a high frequency (0.01s).

Each EV has a battery pack of 16 kWh with a nominal voltage of 325.6 V. These battery packs are made of 88 LEV-50 (LMO) cells mounted in series. Each one has a capacity of 50Ah and a nominal voltage of 3.7V.

As mentioned earlier, we have hundreds of variables to study. To determine modes of use, we are convinced that some variables are more valuable than others. It is for this reason that we will proceed to a selection of parameters that will be described in the following part.

III. SELECTION OF SIGNIFICANT VARIABLES

To select the most significant variables without losing information, we proceeded as follows. At the beginning, we started with the elimination of all the variables that have no relation with the battery usage modes like the temperature of the engine cooling water. So, we kept all the variables related to the batteries (current, voltage, etc.) and the conditions of use of the VE (speed, temperature, etc.).

We have drastically reduced the number of variables to track, going from more than 500 to 140 variables. Nevertheless, the number remains very large and we must still try to reduce it without loss of information.

A. Correlation study

The main purpose of this manipulation is to remove redundancies and keep only one variable on a correlated set. In this part, we have calculated and compared the correlation coefficients between the variables.

According to S. Tufféry [START_REF] Tufféry | Data mining et statistique décisionnelle -4ème édition, 4e édition[END_REF], even if the variables are continuous, "it is always interesting to compare the two coefficients of Pearson (linear correlation) and Spearman (rank correlation), the most reliable of both is the second", in particular to detect nonlinear links.

In the following paragraphs, we will present these two correlation coefficients and the results obtained.

1) Linear correlation coefficient of Pearson "r"

This coefficient is used to study if there is a linear relationship between two variables X and Y. This coefficient is obtained by dividing the covariance between X and Y (σ xy ) by the product of their standard deviation, respectively σ x and σ y .

𝑟 (𝑋,𝑌) = σ xy σ x σ y = 1 n ∑ (X i ̶ X)(Y i ̶ Y) n i=1 √ ∑ (X i ̶ X) 2 n i=1 n √ ∑ (Y i ̶ Y) 2 n i=1 n (1)
Where n is the observation number, X̅ is the mean of X and Y̅ is the mean of Y.

2) Coefficient of correlation of Spearman "ρ"

We also used Spearman's correlation coefficient, which allows us to identify a monotonic connection between the variables, whether linear or not. This technique studies the relationship between the ranks of 2 variables. In other words, we do not use the values of the observations in the calculations but we use their ranks. To calculate the Spearman coefficient of X and Y we can use the equation ( 1) replacing X by rank X and Y by rank Y . If X = (10;13;12;22;25;1), rank X = (5;3;4;2;1;6)

This coefficient can also be given by the following equation.

𝜌 (𝑋,𝑌) =1 ̶ 6 ∑ d i 2 n i=1 n 3 ̶ n (2)
Where n is the number of observations and d i is the difference between the rank Xi and rank Yi .

3) Correlation conditions

We have found that there are several correlated variables. We subsequently considered that two variables (for example X and Y) are highly correlated if they follow the next condition.

(r (X,Y) >0.75 AND ρ (X,Y) >0.8)

(

For the rest of our study, we choose only one variable among a correlated set. And to properly choose the variables to keep, we computed and compared the amounts of information contained in the variables using Shannon's entropy [START_REF] Gray | Entropy and information theory[END_REF].

B. Features selection 1) Shannon Entropy

It is a mathematical function that can measure the amount of information in a signal. Indeed, the larger the entropy of a signal, the more it contains non-redundant information [START_REF] Gray | Entropy and information theory[END_REF]. The entropy value for each variables is given by the following equation [START_REF] Wood | Investigation of battery endof-life conditions for plug-in hybrid electric vehicles[END_REF].

H(X)= ̶ ∑ P i log 2 (P i ) n i=1 (4)
X being a signal of m individuals, containing n different symbols with (n < m). P i is the probability of the appearance of each symbol i with (i  n). ),. then H(X)=1.8424 Before applying this function, the data have to been normalized. The normalization of each variable vector is calculated by equation [START_REF] Kreczanik | Construction of Database on Real World Uses of Electric Vehicles -A French Case[END_REF]. We call Xnorm the normalization vector of X.

Xnorm=

X  min(X) max(X)  min(X) (5)
For each variable, the maximum value takes 1 and the minimum value takes 0. We keep only 4 decimals for Xnorm.

When we have a correlated set of variables, we take only the one that has the greatest entropy.

2) Result of the study of the correlation This followed methodology allowed us to reduce the number of variables to study to 18 non-correlated or weakly correlated input variables. We can find the list of selected variables and a brief description of each one in Table 1. Ambient temperature Ambient temperature °C PKE and RPA are parameters related to the eco-driving and that can also be related to the aggressiveness of driving [START_REF] Nesamani | Development of a driving cycle for intra-city buses in Chennai, India[END_REF], [START_REF] Castignani | Validation study of risky event classification using driving pattern factors[END_REF].

All of these variables summarize the electrical parameters of batteries, how and under which conditions they are used. Nevertheless, this number of variables is still very large to make a good classification able to determine the modes of use.

To deal with this problem, we used the Laplacian score for feature selection technique which allows to select the most significant variables.

3) Laplacian score for feature selection a) Definition and algorithm Overall feature selection methods, we can distinguish two main categories. The "wrapper" methods and "filter" methods. The wrapper techniques evaluate the features using the learning algorithm that will ultimately be employed. Most of the feature selection methods are wrapper methods. Algorithms based on the filter model examine intrinsic properties of the data to evaluate the features prior to the learning tasks. The filter based approaches almost always rely on the class labels, most commonly assessing correlations between features and the class label [START_REF] He | Laplacian score for feature selection[END_REF].

The Laplacian score (LS) is an unsupervised method of selecting significant variables based on the Laplacian Eigenmaps and Locality Preserving Projection. For a set of variables, we measure and compare the LS for each variables and the variables with the highest scores are the most significant.

The application of this method to our variables gave us the results shown in the Fig. 1. This technique is well described in the literature [START_REF] He | Laplacian score for feature selection[END_REF], [START_REF] Benabdeslem | Constrained laplacian score for semisupervised feature selection[END_REF]. The application of the Laplacian Score method cannot help us reduce the number of variables because as shown in the Fig. 1 we have obtained high and very close scores for all the variables. We have also compared the Laplacian scores results with those obtained by the Infinite feature selection technique [START_REF] Roffo | Infinite Feature Selection[END_REF]. The conclusion is the same; all the scores are very close. When all the variables have high scores, we cannot eliminate anyone of them for the risk of losing information. We then decided to work on the 18 variables together, considering them as the most significant variables.

b) Results

IV. DIMENSION REDUCTION

Since the number of variables is still high, we applied the principal component analysis (PCA) method in order to reduce the dimensions by projecting the variables on the PCA axes.

A. Definitions

Principal component analysis is a fundamental method in multidimensional descriptive statistics. It allows simultaneous processing of any number of variables. The aim of the PCA is to project data on a small space dimension by distorting the reality as little as possible [START_REF] Saporta | Probabilités, analyse des données et statistique[END_REF].

The PCA projects data on orthogonal axes that means it is a transformation of variables that can be correlated, to new uncorrelated variables. This process not only reduces the number of variables but also makes the information less redundant.

The choice of the number of components is a very important step in PCA. It depends mainly on the quality of the projection of the observations and the variables, on PCA axes.

B. Choice of components number

To choose the right number of components to keep, there are several adapted practices. We mention here the elbow criterion (break of slope) and the Kaiser [START_REF] Suhr | Principal component analysis vs. exploratory factor analysis[END_REF] law that allow to keep only the axes that have an inertia higher or equal to the average inertia. [START_REF] Beavers | Practical considerations for using exploratory factor analysis in educational research[END_REF] The Fig. 2 shows the inertia for each principal component. In our case, following the two criteria mentioned above, we can choose 4 axes of the PCA that explaining 80% of the total inertia (sum of inertia of the 4 axes). However, it is still necessary to check if at only 4 axes, the quality of the projection of the variables is acceptable. The quality of variables projection is calculated as follows [START_REF] Tufféry | Data mining et statistique décisionnelle -4ème édition, 4e édition[END_REF], [START_REF] Herrera | Initiation à l'analyse factorielle des données : Fondements des mathématiques et interprétations[END_REF]:

QV n = ∑ 𝑐𝑜𝑟𝑟 2 (V,Ax i n i=1 ) ( 6
)
Where QV n is the projection quality of the variable "V" on n PCA axes. corr 2 is the square of the Pearson correlation. V is the data vector of the variable called "V". Ax i is the data vector of the i th PCA axe.

The Table II illustrates the variables projection quality in relation to the number of principal components. The red color is for bad quality and the green color is for a good projection quality. The Kaiser and elbow criterion recommend the choice of 4 principal components for dimension reduction. However, according to Table II, there are some variables that are not well projected. Therefore, to ensure that all the variables are well projected on the axes of the PCA, we chose to keep 7 principal components from 18 (number of variables). The projection quality of all the variables is greater than 75%. This approach has allowed us to reduce the dimensions and eliminate redundancies which will lead to a better identification of the operating modes.

V. IDENTIFICATION OF THE DRIVING CYCLES MODES

This part focuses on the running modes identification.

In fact, we have several thousands of different driving cycles, we call it also runs. We are interested in identifying the similarities and non-similarities between these runs. We seek to obtain, later, several sets of driving cycles. Each set contains a large number of runs that are very similar and at the same time are very different from another set of runs. So we have tested some classification techniques.

A. Unsupervised Classification techniques

The classification methods can be divided into two main families:

-supervised classification where we have already classified elements and where we are interested in adding new elements, -unsupervised classification where we do not have prior knowledge of classes.

In our case, we need an unsupervised classification method to identify VE usage patterns.

Unsupervised Classification is a data-processing technique that seeks to classify a data set by minimizing intra-class distance and maximizing the inter-class distance as much as possible. There are many distance measurement techniques [START_REF] Bora | Effect of different distance measures on the performance of K-means algorithm: an experimental study in Matlab[END_REF] like Manhattan distance and Euclidian distance. In this part, we used the squared Euclidian distance.

There are several methods of unsupervised classification (called also clustering methods), the most well-known ones are the hierarchical ascending classification (HAC) and the k-means [START_REF] Tufféry | Data mining et statistique décisionnelle -4ème édition, 4e édition[END_REF].

1) Hierarchical Ascending Classification (HAC)

The HAC regroups iteratively the individuals by aggregating 2 by 2, the closest elements that allows to build progressively a dendrogram (tree diagram) that regroups at the end all the individuals in a single class. In Fig. 3, we present an example that illustrates the algorithm of the HAC. The algorithm starts with regrouping the observations Ob1 and Ob2 because they are the closest elements. Then we regroup Ob3 and Ob4. The end of calculation is reached when all the observations are regrouped in a single group.

2) K-means

The k-means method consists in grouping the observations in k groups so that the intra-class distances are minimal and the inter-class distances are maximum. The k-means algorithm operates as follows:

After initializing k points (randomly or not) and considering them as centroids, the algorithm distributes the points (observations) in the k classes thus formed according to their proximity to the centroid. Then the algorithm calculates the classes centroids (centers of gravities) and considers them as new centroids. Then, the algorithm repeats these 2 steps until there is no change. The Fig. 4 illustrates how the k-means algorithm works. The Fig. 4(a) shows the initialization of the centroids, at the beginning the points are in blue to say that they do not belong yet to any class. The Fig. 4 (b andc) show how the point be classed among the 2 classes (grey and orange). Fig. 4 (d ande) present the step of centroids recalculations. Fig. 4 (f andg) represent another iteration of the previous steps. Finally, Fig. 4 (h) shows the final result of this classification.

B. Driving cycles classification

To classify our runs, we chose the k-means method since it is faster and at the opposite of HAC technique, it performs a calculation update of the classes centers after each new assignment to follow the evolution of its content. In HAC, if two individuals are placed in different classes, they are never compared again.

Our results revealed that to properly classify our runs with a minimum of classes, we must keep 5 classes. With only 5 classes, we can explain about 60% of the variance (to explain 100% of the variance, the number of classes must be equal to the number of runs: nearly 8000).

The proportion of the explained variance: R² is given by the following equation ( 7):

R 2 = I B I = I B I B +I W ( 7
)
Where I is the total inertia, I B is the inter-class inertia and I W is the intra-class inertia:

I B = ∑ distance(C i ,C) k i (8) I W = 1 n ∑ ∑ distance(l ji ,C i ) m i l ji k i ( 9
)
Where k is the number of classes, C i is the center of class i, C is the center of gravity of all runs, n is the number of runs, m i is the number of runs belonging to class i and l ji is the driving cycles number j belonging to class i.

C. Obtained results

The driving cycles classification into 5 different operating modes gave the results below.

In the Fig. 5, we present the different classes obtained in a reduced dimension (on only 2 axes of the PCA). The data here are centered and reduced. This figure shows that we have a good classification that separates well the different runs. After the driving cycles classification in 5 parts, we considered that a mode of operation is the average of the runs belonging to the same class.

To understand the differences between the different operation modes, we have represented them in Fig. 6. 6 shows the difference between the operation modes according to the 18 variables chosen previously.

To fully understand Fig. 6, we note that we have proceeded to a data normalization so that for each variable the data is between 0% and 100%. 0% being the minimum value and 100% is the maximum value of all the recorded runs.

According to variables distance, average speed and max speed, we can see that modes 1 and 2 represent urban drive cycles and modes 4 and 5 are driving cycles that include highway parts. In addition, according to variables RPA, Average acceleration, average deceleration and PKE, we can see that mode 4 describes a more aggressive behavior compared to mode 5 and similarly for mode 2 compared to mode 1. This may also be related to the traffic density.

We can also notice that, for Qperkm and ambient temperature variables, all the modes have almost the same coordinates. This means that the obtained operating modes do not depend or depend very little on these variables. This methodology allowed us to obtain several driving cycles classes that depend on different variables. In a next step, we will study the effect of these driving modes on batteries aging.

VI. CONCLUSION

The present work starts from real-life recording of the batteries solicitation to determine the different operating modes of the batteries in automotive application. The database, which is constructed from about 2 years of data recording of 10 EV, initially contains more than 140 parameters with potential impact on the batteries. By using several selection tools based on correlation coefficients of Pearson and Spearman and Shannon entropy, we decrease the number of parameters to 18. Laplacian Score method cannot manage to reduce anymore this number which attests the difficulty to easily characterize a mode of use of EVs. For a better quality of classification, we used the PCA technique which made it possible to reduce the dimension and to eliminate the redundancies. A K-means analysis enabled us to find 5 different modes of use. Each mode is defined by the value of 18 parameters previously identified. The tools developed in this work will allow to study the potential links between the batteries aging rate and the identified modes of use.

  For example X= (a;b;a;c;a;b;d), so m=7, n=4 and

Figure 1 :

 1 Figure 1 : Laplacian score for each variable

Figure 2 :

 2 Figure 2: Inertia for each principal component

Figure 3 :

 3 Figure 3: Example of HAC

Figure 4 :

 4 Figure 4: k-means algorithm illustration

Figure 5 :

 5 Figure 5: Representation of the classification result on the first two principal components

Figure 6 :

 6 Figure 6: Characteristics of each running mode Fig.6shows the difference between the operation modes according to the 18 variables chosen previously.

TABLE I

 I

		.	SELECTED VARIABLES
	Variable name	Description	Unit
	Soc_init	State of charge at the beginning of driving cycle	%
	Max charge current	Maximum current in recovery energy phase	A
	Average charge Power	Average recuperated power	W
	Average decel	Average deceleration	m.s -2
	Qperkm	Charge quantity per km	Ah/km
	Qchar	Recuperated charge	Ah
	Dist	Distance	km
	Q disch	Consumed charge	Ah
	Average speed	Average speed	km/h
	Max speed	Maximum speed	km/h
	Max discharge current	Maximum discharge current	A
	Effective current	Effective current	A
	Average discharge power	Average power when discharging	W
	Relative positive acceleration	Relative Positive Acceleration (RPA)	m.s -2
	Average acceleration Average acceleration	m.s -2
	Positive kinetic energy	Positive acceleration Kinetic Energy (PKE)	m.s -2
	Q auxiliaries	Charge consumed by auxiliaries	Ah

TABLE II

 II

	.	NUMBER OF COMPONENTS EFFECT ON THE QUALITY OF
			VARIABLES PROJECTION
					Number of axes
			1	2	3	4	5	6	7
	Initial SoC		1% 1% 1% 95% 95% 99% 99%
	Max charge current	43% 51% 53% 53% 68% 75% 77%
	Average charge Power	47% 66% 69% 70% 70% 75% 75%
	Average decel		0% 84% 84% 84% 87% 94% 94%
	Qperkm		1% 3% 74% 74% 78% 80% 89%
	Qchar		52% 52% 60% 60% 76% 84% 90%
	Distance		61% 86% 86% 88% 88% 94% 94%
	Q disch		68% 87% 88% 90% 91% 97% 97%
	Average speed		74% 79% 81% 81% 96% 96% 97%
	Max speed		85% 86% 87% 88% 88% 89% 89%
	Max discharge current	66% 68% 73% 75% 81% 83% 83%
	Effective current		81% 92% 94% 94% 95% 96% 97%
	Average discharge power	80% 83% 85% 85% 94% 94% 97%
	Relative Positive Acceleration 41% 91% 92% 92% 96% 96% 96%
	Average acceleration	0% 88% 88% 89% 89% 94% 94%
	Positive Kinetic Energy	9% 79% 82% 83% 92% 92% 92%
	Q auxiliaries		17% 25% 75% 76% 77% 81% 83%
	Ambient temperature	1% 1% 57% 59% 67% 67% 99%

ACKNOWLEDGMENT

We like to thank the region Auvergne-Rhone-Alpes for its financial support for this work.


UMRAE, CEREMA Jean-Rémy Gloaguen
email: jean-remy.gloaguen@orange.fr

Mathieu Lagrange

STUDY OF THE NON-NEGATIVE MATRIX FACTORIZATION BE-HAVIOR TO ESTIMATE THE URBAN TRAFFIC SOUND LEVELS

Keywords: road traffic, non-negative matrix factorization, traffic noise mapping

The advent of low-cost acoustic sensor networks in cities raises new interesting approaches for improving the monitoring of the acoustic quality of cities. Many innovative approaches are developed to improve knowledge on sound environments: sound environment recognition, sound source detection, etc. In order to improve the road traffic noise mapping, the use of a specific version of the Non-negative Matrix Factorization (NMF), named thresholded initialized NMF, as a source separation method to estimate the sound level of road traffic from measurements, has proved to be a successful approach. This paper proposes to further detail the functioning of the thresholded initialized NMF on a corpus composed of urban sound scenes mixing traffic and specific interfering components with calibrated sound levels in order to better understand its behavior according to the different sources encountered. The study reveals the different performances of this approach depending on the noise levels of the interfering sources and their proximity to the urban traffic spectrum.

Introduction

Low-cost acoustic sensor networks are currently deployed in cities to assess the urban sound environment as the DYNAMAP project [?] in Italy or the CENSE project [?] in France. The use of such networks allows innovative approaches to better estimate the soundscape through, for instance, the urban sound environment classification [?]. One application of interest is the improvement of the traffic noise mapping ordered by the European Directive 2002/EC/49. These maps are currently generated from predictive models and collected traffic data to estimate the A-weighting traffic sound level through the day, L DEN , and the night, L N . The use of these networks could facilitate the updating of the traffic maps or even the generation of dynamic maps by the assimilation of the measured data with the predicted sound levels [?]. Prior to data assimilation, the issue is to correctly estimate the traffic sound level from acoustic measurements [?, ?]. As the urban sound environment is a complex environment gathering lots of different sounds (car passages, voices, whistling bird, car horn, airplanes, etc.) that overlap in time, the estimation of the traffic sound level based on acoustic measurements is not a trivial task [?]. Although near major roads, traffic is predominant, there are many places where it overlaps with other sound sources that contribute significantly to the overall sound levels. To circumvent this issue, Socoró et al. propose an anomalous noise events detector [?]. It consists in detecting in each time frame the unwanted sound sources from labeled recordings, i.e. that are not related to the traffic component. Those time frames are then discarded in order not to take them into account during the estimation of the traffic sound level. An alternative approach has been proposed in Gloaguen et al. [?], which is based on the blind source separation paradigm to reliably estimate the traffic noise level even when the traffic is not dominant in the mixture. It consists in separating the contribution of the traffic from the other sources within a polyphonic scene with the help of the Non-negative Matrix Factorization framework (NMF). In their study, the authors present a new approach of this method, named thresholded initialized NMF (TI NMF) whose estimate of the traffic sound level exceed in precision those of state-of-the-art approaches (supervised and semi-supervised). TI NMF then makes it possible to estimate the noise level with an estimation error less than 2 dB1 . To do so, their study is based on simulated sound mixtures covering different sound environments (park, quiet street, noisy street, very noisy street) whose realism has been tested on a perceptual test to ensure that the sound mixtures are perceptively equivalent to audio recorded in the streets [?]. One major advantage of this approach is its application in a wide range of urban areas, even where the traffic noise is relatively low compared to the remaining contributions. In addition, NMF is well suited for monaural sensor networks. Here, to better understand the TI NMF behavior, this method is tested on a new corpus of sound mixtures mixing a traffic component with a calibrated sound level and specific sound classes as perturbators. The use of simulated sound scenes allows the authors to design a rigorous experimental validation protocol which offers a high level of control on the design of the scenes and the knowledge of the exact contribution of the traffic component (L p,traf f ic ). The remaining of the paper is organized as follows. Section ?? details the technical aspects of TI NMF. Section ?? describes the urban sound mixtures and the experimental protocol setup. Section ?? presents and discusses the outcomes of the numerical results.

Non-negative Matrix Factorization

Description of NMF

Non-negative Matrix Factorization is a linear approximation method introduced by Lee and Seung, [?], which can be used to approximate the spectrogram Ṽ (obtained using a Short-Term Fourier Transform) of an audio file, V, ∈ R + F ×N as V ≈ Ṽ = WH where W ∈ R + F ×K is the dictionary (or basis) matrix composed of audio spectra and H ∈ R + K×N is the activation matrix, which summarizes the temporal evolution of each element of W. As the constraint of non-negativity of W and H is considered, NMF allows only additive combinations between the element of W, thus inducing a part-based representation. The choice of the dimensions is often made so that F ×K +K ×N < F ×N [?]. NMF is then considered as a low rank approximation method. However, this constraint is not mandatory. To estimate the quality of the approximation, an objective function is minimized min

H≥0,W≥0 D V|| Ṽ = F f =1 N n=1 d β V f n | [WH] f n .
(1)

The operator d β (x|y) is a divergence calculation and usually belongs to the β-divergence class [?] in which the well known Euclidean distance and the Kullback-Leibler divergence.

Thresholded initialized NMF

Thresholded initialized NMF has been proposed in [?] and is based on the unsupervised NMF. Usually in unsupervised learning, W, as H, is initialized randomly. In TI NMF, an initial dictionary, W 0 dedicated to a specific sound source, is built from a learning database, see section ??. Then NMF is performed where W and H are updated alternatively. W is therefore updated with a forced initialization with a priori knowledge but is adapted to the actual content of the scene under study. Among the different algorithms proposed to solve ??, the multiplicative update of W and H is chosen as it has been well studied in the literature and ensures convergence of the results. It can be found in [?]. After N iterations, a measure of similarity D W 0 ||W between W 0 and the obtained dictionary W for each element k is computed using a cosine similarity metric,

D W 0k ||W k = W 0k .W k ||W 0k ||.||W k || . ( 2
)
D W 0k ||W k = 1 means that the k-th element of W is identical to the k-th element of W 0 .
On the contrary, D W 0k ||W k = 0 means that the elements are very different. This measure has the advantage to be bounded between 1 and 0 and to be invariant with respect to scale. The elements in W that can belong to W traf f ic are selected by a hard thresholding method. It is defined as

W k ∈ W k,traf f ic if D W 0k ||W k > t where t is a fixed threshold.

Experimental protocol

In order to study the TI NMF behavior according urban sounds sources, the experimental protocol, the databases and the experimental factors involved in the experiment are presented, each of these experimental factors having multiples modalities, see Fig. ??.

Test databases

The test database is designed with the sound scene synthesizer SimScene2 [?], a simulator that creates monaural sound scenes by sequencing audio samples that come from a database of isolated sounds[?]. The database3 used is presented in [?]. This offers a controlled framework to design at low cost a wide diversity of sound environments in which all the traffic components are known, thus allowing the computation of the reference level. This database is composed of 3 sub-corpus of 25 audio files each lasting 30 seconds. Each sub-corpus is characterized by an interfering sound class which can be animals (an.), composed of barking dogs and whistling birds, humans (hu.), composed of crowd noises and voices, and transportation (tr.), composed of train, tramway and plane sounds. Each sound mixture is summed with a traffic component (the sum of the road traffic background noise and the sound events generated by the passing car class) that makes the estimation of the traffic level more difficult. In each file, the traffic component is present. To test different scenarios, each audio file is duplicated with the traffic sound level of the entire sound scene, L p,traf f ic , fixed to a calibrated level according to the sound level of the interfering class, L p,interf ering such as T IR = L p,traf f ic -L p,interf ering with the Traffic Interference Ratio T IR ∈ {-12, -6, 0, 6, 12} dB. The range of these values is large but in the urban environments, the T IR typically lies between -6 dB and 12 dB [?]. The case T IR = -12 dB is then an extreme case to study the NMF behavior. When T IR < 0 dB, the traffic component is less present than the interfering class. On the contrary, for T IR > 0 dB, the traffic class is louder than the interfering class. The total number of scenes in the test database is then 375 (3 sub-corpus × 25 scenes × 5 T IR values) leading to a full duration of 3 hours and 15 minutes.

NMF experimental factors

The Figure ?? presents the different steps involved in the NMF process. The dictionary design requires a train database composed of 53 audio samples of passing cars. A four-step process is realized to generate different versions of dictionaries with a sub-sampling of the audio spectrograms by a windowing of w t seconds (w t ∈ {0.5, 1} second), a rms calculation of each window and a K-means clustering algorithm reducing the number of spectra to K ∈ {25, 50, 100, 200}. Each basis vector of W is normalized such as ||W k || = 1 with || • || the -1 norm. The 8 versions of the dictionary are then used as the initial dictionaries W 0 . A full description of the dictionary design can be found in [?]. 100 iterations are performed with the Euclidean distance and the Kullback-Leibler divergence. The spectrogram V and the dictionary W are expressed with third octave bands (F = 29). This coarser method allows us to reduce the dimensionality and thus decrease the computation time. Also, it is a suited representation to this sound environment as third octave bands are widely used in the urban acoustic field, compared to MFCCs for instance. The range of threshold values is set between 0 and 1 with a 0.01 increment step. Considering the experimental factors (sub-corpus, T IR, w t , K, EUC dist./K-L div., t) derived from the different modalities of each experimental factor, 24240 settings are performed. For each setting, the estimator is performed on the 25 scenes of a sub-corpus. For one sound scene, the estimated traffic sound level, Lp,traffic , of the entire scene is calculated, Lp,traffic = 20 × log 10 prms p 0 where p rms is the effective pressure deducted from the estimated traffic spectrogram Ṽtraffic and p 0 is the reference sound pressure, p 0 = 2 × 10 -5 P a. The A-weighting of the sound levels is not considered here as it decreases the low frequencies levels where the road traffic components are mainly present.

Metrics

For each setting of experimental factors, 25 values of Lp,traffic are obtained and compared to the 25 exact sound levels, L p,traf f ic . Its performance is assessed through the calculation of one reference metric, the Mean Absolute Error (M AE). It expresses the quality of the long-term reconstruction of the signal and is defined as

M AE = 25 m=1 |L m p,traf f ic -Lm p,traf f ic | 25 . ( 3
)
This error is calculated for each sub-corpus and each T IR value. Then a mean M AE, mM AE, Figure 2: M AE errors (??) and distribution of relatives error (??) on each sub-corpus and T IR value.

is calculated on the entire test database. This metric expresses the average performance of NMF for a unique combination of modalities.

Results

Among all the combination of modalities, the one retained its the one generating the lowest mM AE error in order to facilitate the study of the results. It is obtained with w t = 0.5 s, K = 200, β = 2 and t = 0.42 with mM AE = 2.57 (± 2.18) dB. thus, the optimal modalities found here are not exactly the same than the one found [?], as the corpuses are completely different. That being said, the TI NMF performs consistently well, motivating the authors to study further its behavior.

The errors on each sub-corpus and for each T IR value are displayed in Fig. ??. The error at T IR = -12 dB, as it is an extreme case where the traffic component is low, are the most important for each subcorpus with high standard deviation. For animals and transportation, the M AE errors exceed 3 dB. With the exception of the transportation sub-corpus at T IR = -6 dB, the rest of the errors are inferior to 3 dB. The errors for animals sub-corpus are lower than the two other sub-corpuses as it contains interfering sound events relative to bird's whistles, in a highest frequencies domain than the traffic spectra, and dog barking, more brief in time. One observes that the M AE errors for this sub-corpus decrease as the T IR values increase. On the contrary, for humans and transportation sub-corpus for T IR ≤ 0 dB, the M AE errors decrease too but increase when T IR > 0 dB. In parallel to the bar distribution of the M AE errors, the distribution of relative errors (∆ Leq = Leq,traffic -L eq,traf f ic ) is added in Fig. ?? with boxplots. When ∆ Leq > 0, the traffic sound level estimated by TI NMF is over-estimated while it is underestimated when ∆ Leq < 0. For the animals sub-corpus, one notices that TI NMF underestimates the traffic sound level for all T IR values. For the two others sub-corpus, TI NMF mostly overestimates the traffic sound level when T IR ≤ 0 dB and then underestimates it when T IR > 0 dB. To better understand the TI NMF behavior, the evolution of the M AE errors according to the threshold and the distance D(W 0 ||W) shape are displayed, see Fig. ??.

In Figure ??, the distances D(W 0 W) are sorted in descend order for each T IR values and subcorpus. Its behavior is variable according to the sub-corpus and the T IR value. With the fixed threshold t = 0.42, only less than 100 elements from the obtained dictionary W are considered in W traf f ic when T IR = -12 dB. A major part of W 0 diverges from the original traffic spectra to simulate the interfering source. The more the traffic is predominant, the more the number of traffic elements in W traf f ic growths. For T IR = 12 dB, it is more than 180 elements that are considered. The traffic component is then described by TI NMF with a greater precision. The evolution of D(W 0 W) is more influenced by the T IR values than by the interfering source as almost the same number of elements are considered in W traf f ic whatever the sub-corpus. TI NMF makes it possible to adapt the dictionary by learning it for each scene. With the fixed threshold it is also a variable number of elements in the W traf f ic that are considered, discarding the other elements that diverge too much from the original traffic spectra on W 0 . Figure ?? displays the evolution of the M AE errors according to the threshold which allows us to better apprehend the estimation errors. It states the existence of optimal thresholds in each different case. The lower the T IR value, the higher the threshold has to be, while it has to decrease when the traffic component becomes predominant. At T IR = -12 dB, the optimal threshold is inferior to the fixed threshold t = 0.42 for animals sub-corpus while it is superior for the two other sub-corpuses. As a result, for the first case, TI NMF, with t = 0.42, does not take into account a sufficient number of elements in W traf f ic which leads to an underestimation of the traffic sound level as can be seen in Fig. ??. On the opposite, as the optimal threshold for the two others classes is higher, TI NMF includes too many elements that includes some elements of the interfering sound classes. This behavior is due to the interfering sound source being more different in the animals sub-corpus than in the humans and the transportation sub-corpuses. Overall, this leads to an overestimation of the traffic sound level. When the T IR values increase, the traffic component becomes more important in the sound mixtures and in the dictionary W. When T IR = 12 dB, the optimal error is obtained for a lower threshold to almost take into account all the element of the dictionary W. TI NMF with t = 0.42 discards some traffic spectra which generates the underestimation of the traffic component.

Conclusion

The thresholded initialized Non-negative Matrix Factorization behavior has been studied on urban sound mixtures in order to better apprehend its performances according different urban sound sources dealing with the estimation of the traffic sound level. With this method, a dictionary W 0 is initialized with road traffic spectra and optimized to fit the data at hand. The traffic elements that are similar to the road traffic spectra are then extracted by hard thresholding. TI NMF makes it possible to learn a specific dictionary on each scene and to only consider the least divergent part of the dictionary with the thresholding step. The sound database used here is an artificial build-up of a traffic component and different interfering sound classes (animals, humans, transportation) with calibrated sound level in order to propose a controlled framework. Among the different modalities of the experimental factors, only one combination was retained (K = 200, w t = 0.5 s, Euclidean distance, t = 0.42). This choice was made in order to reduce the complexity of the study and to propose the most efficient method, whatever the nature of the interference sound source, so as not to add a classification step before.

With this combination, the performances are changing according to the predominance of the traffic and the different interfering sound sources. In the case where the interfering source differs from the road traffic by its spectral and temporal shapes, the errors induced on the estimation of the traffic sound level are low whatever its presence (M AE < 2 dB). These errors tend to decrease with the increase of the traffic presence. In this case, TI NMF tends to underestimate the traffic sound levels because of the fixed threshold that is then too high and does not allow a sufficient number of traffic elements to be taken into account in W traf f ic . In the case where the spectral shape of the sources are similar to the ones of road traffic, two types of errors appear. In the case where T IR ≤ 0 dB, the fixed threshold is considered too low which causes the consideration of interfering elements in the traffic noise level calculation and thus its overestimation. When T IR is positive, the threshold is now considered as too high which leads to an underestimation of the traffic sound levels.

As a result, the TI NMF performances with a single fixed over the entire corpus make it possible to perform reasonably well without the need of a classification step. It then presents itself as a compromise in its performances according to the predominance of the traffic and its similarity with other interfering sound sources. If the sound scenes are composed of the traffic source and an interfering source, the urban sound environments are the result of the mix of all these sources sometimes emitted simultaneously. Keeping a fixed threshold remains necessary in order to better determine the road traffic sound level in all the cases.

The experimental protocol and the evaluated estimators have been implemented with the Matlab software. For reproducibility purposes, the code is available online4 . The evaluation database composed of multiple samples of urban sounds is also made available5 for the research community with interest in detection, separation and recognition tasks of urban sound sources.

Figure 1 :

 1 Figure 1: Block diagram of the NMF estimator with the dictionary design composed from a second sound database.

Figure 3 :

 3 Figure 3: Distances D(W 0 W) sorted in descend order and M AE errors evolution according threshold values.

Tool available at: https://bitbucket.org/jean-remy_g/trafficsoundlevelestimation

Open-source project available at: https://bitbucket.org/mlagrange/simScene

available at: https://zenodo.org/record/1213793

ICSV26, Montreal, 7-11 July 2019

https://github.com/jean-remyGloaguen/ICSVnmf2019

https://zenodo.org/record/1145855


Lina Konstantinopoulou

Alessandro Coda

Franziska Schmidt

Specifications for Multi-Brand Truck Platooning

Keywords: truck platooning, specifications, automated driving peloton de camion, spécifications, conduite automatisée

Platooning technology has made significant advances in the last decade, but to achieve the next step towards deployment of truck platooning, an integral multi-brand approach is required. It is the ambition of ENSEMBLE to realize pre-standards for interoperability between trucks, platoons and logistics solution providers, to speed up actual market pick-up of (sub)system development and implementation, and to enable harmonization of legal frameworks in the member states. This paper provides with definition of the specifications of the whole multibrand truck platooning concept to be implemented within the testing and demonstration trucks of the 6 OEMs. It describes the functional architecture, captures all minimum set of operations layer requirements and tactical layer specifications for Platoon level A. The building blocks of truck platooning consist of in-vehicle requirements (Longitudinal, sensors, HMI interaction), infrastructure (V2I), information among trucks in platoon, and platooning strategy (coordination mode, gap regulation, formation, dissolution, and vehicle mix).

Introduction

Platooning technology has made significant advances in the last decade, but to achieve the next step towards deployment of truck platooning, an integral multi-brand approach is required. Aiming for Europe-wide deployment of platooning, 'multi-brand' solutions are paramount. It is the ambition of ENSEMBLE to realise pre-standards for interoperability between trucks, platoons and logistics solution providers, to speed up actual market pick-up of (sub)system development and implementation and to enable harmonisation of legal frameworks in the member states.

Main goal of ENSEMBLE

The main goal of the ENSEMBLE project is to pave the way for the adoption of multi-brand truck platooning in Europe to improve fuel economy, traffic safety and throughput. This will be demonstrated by driving up to seven differently branded trucks in one (or more) platoon(s) under real world traffic conditions across national borders. When evaluating the different platooning projects such as CHAUFFEUR II (2000II ( -2003)), KONVOI (2005KONVOI ( -2009)), SARTRE (2009SARTRE ( -2012)), i-GAME (2013 -2016), COMPANION (2013COMPANION ( -2016)), AUTONET2030 (2013AUTONET2030 ( -2016)), ROADART (2015ROADART ( -2018)), CONCORDA (2017 -2020), it can be seen that these projects mostly concentrate on developing the in-vehicle platooning technology, whereas later projects more concentrate on either a specific technological challenge (e.g. antennae design and placement) or on the use of platooning technology (e.g. platoon coordination). With respect to use cases and in-vehicle architectures, many commonalities are seen on a high level. However, details are often not published. This also holds for the low-level controllers used in the different projects. Moreover, tactical layer functionalities and operational layer functionalities have mostly been implemented as one 'controller', i.e. there was no separation between 'common' and 'truck specific' functionalities, which is needed for ENSEMBLE's tactical and operational layers. Hence, a clear task is reserved for ENSEMBLE to separate these functionalities in a way that the technology is still usable for all OEMs. Besides that, the impact of non-homogeneous platoons is still unclear. Heterogeneous platooning may stem from different sources: different operational implementations (spacing policies, control algorithms and information used for control, for instance), different vehicle capabilities in accelerating and decelerating (vehicle total mass, available engine power, brake capacity). Additionally, road profile may affect platoon performance. Despite the substantial academic work on platooning, applied control design for (heterogeneous) platooning is still an open issue. Only very limited publications deal with implementation relevant aspects and/or heterogeneity of platoons. This thus is still an open area also for ENSEMBLE.

Platooning levels and SAE

In order to break down the complexity of deploying multi-branded truck platooning on public roads different platooning Levels are defined. The Platooning Levels facilitate a stepwise approach to deployment of platooning on public roads, where the "first" Platooning Level defined in ENSEMBLE can be deployed in the near future. The idea of Platooning Levels has emerged since the commonly accepted automation levels of the SAE J3016 have shortcomings when applied to platooning. The first level in the SAE levels of driving automation involves either longitudinal or lateral vehicle automation, whereas the driver is responsible to detect safety-critical events and take appropriate action. Level 2 involves both longitudinal and lateral automation, with the same driver responsibility as in level 1. In level 3, the driver responsibility is decreased to only act upon a warning by the automation system. Only in level 4 and higher, the driver has no driving task anymore for part or all of the journey. Truck platooning, however, involves driving at short inter-vehicle distances for an extended period. Therefore, the driver cannot be held responsible for timely intervention in case of safety-critical events such as hard braking. Furthermore, the platoon as a whole can be seen as a system of interconnected systems with specific requirements. Hence, the first three SAE automation levels are not directly applicable for the platooning application. Consequently, the need arises to create a different automation level classification for heavy duty vehicles that considers the explained needs of Platooning. The definition of "platooning levels of automation" will comprise elements like the minimum time gap between the vehicles, whether there is lateral automation available, driving speed range, operational areas like motorways, etc.. Three different levels are seen for now -called platoon level A, B and C.

Functional Architecture

The white label truck representing a brand-less truck that has all the described specifications, Figure 1 gives an overview of the elements of the white label truck. The white label truck on the one hand comprises specifications: these are mainly elements specifically added for platooning. On the other hand, it comprises requirements: here OEM specific systems or connections to OEM specific systems are involved

Platooning layers

The concept of the ENSEMBLE platooning system consists of a hierarchical system, with interacting layers. The envisioned concept is presented in Figure 2. on highway traffic flow and infrastructure, employing cooperative ITS cloud-based solutions. In addition, the routing of vehicles to allow for platoon forming is included in this layer. The strategic layer is implemented in a centralised fashion in so-called traffic control centres. Long-range wireless communication by existing cellular technology is used between a traffic control centre and vehicles/platoons and their drivers.  The tactical layer coordinates platoon forming (both from the tail of the platoon and through merging in the platoon) and platoon dissolution. In addition, this layer ensures platoon cohesion on hilly roads, and sets the desired platoon speed, inter-vehicle distances (e.g. to prevent damaging bridges) and lateral offsets to mitigate road wear. This is implemented through the execution of an interaction protocol using the shortrange wireless inter-vehicle communication (i.e. V2X). In fact, the interaction protocol is implemented by message sequences, initiating the manoeuvres that are necessary to form a platoon, to merge into it, or to dissolve it, also considering scheduling requirements due to vehicle compatibility.  The operational layer involves the vehicle actuator control (e.g. accelerating/braking, steering), the execution of the manoeuvres, and the control of the individual vehicles in the platoon to automatically perform the platooning task. Here, the main control task is to regulate the inter-vehicle distance or speed and, depending on the Platooning Level, the lateral position relative to the lane or to the preceding vehicle. Key performance requirements for this layer are vehicle-following behaviour and (longitudinal and lateral) string stability of the platoon, where the latter is a necessary requirement to achieve a stable traffic flow and to achieve scalability with respect to platoon length, and the short-range wireless inter-vehicle communication is the key enabling technology. The white-label truck concept encompasses both the tactical and the strategic layer, whereas the operational layer will be brand specific and the services layer will focus on logistics, hence being unbranded. Hence, ENSEMBLE multi-brand truck platooning concept takes into consideration Platoon level A (e.g. driver responsibility) which will form the basis of the intended demonstration at the end of the project on public road.

Multi-Brand truck Platooning Specifications

Summarizing, D2.4 captures all minimum set of operations layer requirements and tactical layer specifications for the white label truck platooning concept. The building blocks of truck platooning consist of in-vehicle requirements (Longitudinal, sensors, HMI interaction), infrastructure (zone policy), information (ranging, V2V and V2I exchange, and data sharing), and platooning strategy at tactical layer (coordination mode, gap formation, dissolution, and vehicle mix).

The tactical layer coordinates the actual platoon forming (both from the tail of the platoon and through merging in the platoon) and platoon dissolution. Related to platoon manoeuvre coordination, specifications indicate that the platoon shall have awareness of the status of the coordinated gap opening on platoon level and the target vehicle reports to the following vehicle the status of the gap opening procedure. During gap opening, the speed of the leader vehicle is distributed over the whole platoon. The tactical layer will gather several platoon status items (e.g. ego vehicle position) and distribute this over the platoon in a hopping type of method and this information is updated with a rate of 1 Hz. The tactical layer shares the vehicle status information in an equal method within the platoon as the platoon status and this information is updated with a rate of 1 Hz. The setpoint limit/advice function will be send to the operational layer the combined advised value for the vehicle set speed and time gap as processed from information from the strategic layer and the platoon status. The requirements simulation process is supported by first-principles simulation in order to perform a reality check on the feasibility and the relevance of the specifications.

In order to allow an interaction with the trucks in the platoon, a communication link must be established between the platoon participants. The decentralized tactical layer running locally in the trucks needs information from the other trucks. Therefore, also the state machine on vehicle level is introduced.

For Platoon Level A, the Road Side Unit have been selected for the implementation of communication between the platoons and the infrastructure. The information that is needed is the ability for the individual vehicles of the platoon to receive communications on policy based on zone (zone policy or geofencing) and constantly to be up to date (refresh period to be defined). In addition, the ability for the individual vehicles of the platoon to adjust speed based on zone policy and interdistance based on zone policy is also an additional specification.

The requirements for the in-vehicle hardware components which are specific for platooning can be grouped into the following categories:

 HMIthe driver interface to the vehicle and in particular the platooning solution  Longitudinal control system consists of sensors, control computation and communication and control actuation components.

The common HMI-logic should function as the "lowest common denominator" for each OEM's HMI-design for platooning, regardless truck brand. The purpose of the common HMIlogic is to provide a structure for coherent interactions between the driver and the platoon system and still allow for OEM specific solutions.

Longitudinal control requirements are divided among requirements for how the minimum inter vehicle time gap is selected, requirements for safely handling braking in the platoon, requirements for how to increase the inter vehicle time gap in a safe way and requirements for how to close gaps and keep the platoon together. The latter one describing the platoon cohesion functionality is summarized in two requirements, where the first requirement is aiming for solving an existing cohesion issue, whereas the second requirement is about avoiding cohesion issues to occur. As regards sensing specifications, this report gave a focus to the sensor data and associated sensors required for a white label solution to assess the environment and which are specific for platooning. It was concluded that radar technology best fulfils the requirements for distance keeping in a platoon. V2X communication is also mandatory inside a platoon and can be used also as sensor functionality.

Axle loads and weigh-in-motion

Weigh-in-motion systems

Loads (axle loads or GVW) of the trucks with one multi-brand platoon can be obtained through various means:

 The loads can be obtained from weigh-in-motion systems installed on the road infrastructure, meaning in-pavement sensors or bridge weigh-in-motion. These solutions are especially interesting for road authorities, as a direct access to the data is always possible.  Axle loads can also be obtained through on-board weighing which is installed on the axles on trucks, often originally on modern trucks. This data may be used directly by trucks for their route planning, or by road authorities by modalities for this data sharing have been installed.

Utility of loads

Axle loads do have several uses, for example:  Some parts of the road infrastructure may not be able to cope with all types of platoons, because of the associated damage. For these, geofencing may be linked to the GVW and axles loads of the trucks with the platoon. In this case, communication V2I will be needed, and I2V for giving the driving instructions.  Axle loads and GVW are also important for the platoon itself. Indeed, braking distances are linked to the carried loads, and the braking power of the truck. Therefore, it might be better to put a light truck with good braking distance, after a heavily loaded truck with weak braking power.

Conclusions and next steps

Finally, specifications are going to provide the specified functionalities for the project to implement and demonstrate in 2021. Verification and validation phase of the functionality of the equipped vehicles will be verified against the specifications and the developed functionality will be compared to the intended multi-brand functionality to validate the results. A list of KPIs like e.g. in impact of platooning on traffic flow, bridges, other road-user's behaviour, impact on the environment, possible business cases will be mapped against requirements. Finally, the requirements are consolidated towards pre-standards and recommendations and guidelines and are developed for future policy and regulatory frameworks for the wide scale implementation of multi-brand platooning. The updated report will provide with a mapping and gap analysis between the specified requirements and specifications and standardisation and regulation. The iteration process to validate and modify the specification during the whole project life-cycle is an essential part of the work.

There are still however open issues that need to be resolved and assumptions taken. The requirements are written as if the system is responsible because that is still the agreement for Platooning Level A and vision of the ENSEMBLE project. If the system is responsible it will probably put very strict requirements on the system, such as high accuracy brake capacity estimation and very high reliability on always being able to brake. This remains the task to be further defined for Platoon level B and C the definitions and use cases detailed in D2.3 and their respective requirements and specification which will be reflected in D2.5. For the platooning demonstration it is planned to use the actuators which are present in state-of-the-art vehicles. Thus, there are no specific requirements for the time being. This could change over time once the results of the HARA and SOTIF analysis are available. The communication requirements are already documented in other reports.

The HMI-logic presented in this deliverable is based on the current knowledge from platooning and from general Human factors guidelines in the field of driver-automated vehicle interaction. The HMI-logic has not been evaluated and validated, for example in field tests or in simulator studies and, therefore, should be regarded as a draft and subject to changes as platoon systems are tested and evaluated from technical as well as from user (driver) point of view. Moreover, the HMI-logic is on a high level and does not stipulate specific messages, icons, symbols, colors or if and how multi-modal output (sounds and haptic) should be used to enhance the driverplatoon system interaction. These issues are important to investigate further once the overall HMI-logic is in place and be subjects for standardization. The results from the interviews with platoon drivers indicated that the verbal communication (via radio) between the drivers was important to maintain the platoon and to handle situations, such as Cut-ins, obstacles ahead, traffic at exits and entries etc. However, it is most likely drivers in future platoons speak different languages and don't understand each other. Therefore, the safety of a platoon should not be dependent on verbal communication between the drivers.

Another open issue is how drivers can recognize which truck(s) on the road is a pending coplatoon truck. This is also the case while driving in a platoon, i.e. how to know that the truck in front of the ego-truck is part of the platoon (and not a cut-in). A subsequent question is if and how other co-road users need to be informed about platoon driving on the road. Driver and system roles and task, the term "responsibility" is deliberately not used, because "responsibility" infers legal matters and not HMI-matters. The responsibilities of the driver and the platoon system in different use cases and possible critical incidents should be investigated from a legal point of view (not from an engineering or HMI-point of view).

Another consideration is that the definition of the specifications of the whole multi-brand truck platooning concept have not yet been mapped across functional safety analysis and SOTIF in order to assure that the white label truck platooning concept functions and acts safely during normal operations. The ENSEMBLE project will analyse the safety risks related to both functional safety and SOTIF and derive requirements to lower these risks to an acceptable level. Since these activities will not only define requirements for hazards arising from E/E malfunctions but also address hazards resulting from performance limitations or insufficiencies of the function itself, the safety activities carried out for the project are enough to have a safe platoon deployment on public roads. Functional Safety and SOTIF activities, will define new requirements and may restrict current Platoon Level A definition.

In addition, to that the project is going to assess how these specifications have impact to weight in motion strategies. Moreover, another important issue is that regulation and requirements by the road authorities and member states might also generate additional requirements and might impact testing and verification of trucks platooning systems on the roads. The project has also foreseen to organize in second quarter of 2019 a common workshop among the European Truck Platooning challenge (ETPC), C-Roads Platform, CONCORDA, CEDR in order to validate the ENSEMBLE requirements and to ensure convergence and agreement on the V2I topic and to suggest a unique proposal for the European Commission.

Figure 1 -

 1 Figure 1-Elements on the white label truck

Figure 2 -

 2 Figure 2 -ENSEMBLE platooning layersThe different layers have the following responsibilities: The service layer represents the platform on which logistical operations and new initiatives can operate.  The strategic layer is responsible for high-level decision-making regarding scheduling of platoons based on vehicle compatibility and Platooning Level (see below), optimisation with respect to fuel consumption, travel times, destination, and impact

Table 1 -High level platoon levels overview Platoon level A

 1

	Platoon level B	Platoon level C


domain: phys spi

text:
Je ´ro ˆme Minet

Se Abramson

Bruno Bresson

Alexandre Franceschini

Henri Van Damme

Nicolas Lequeux
email: nicolas.lequeux@espci.fr

Organic calcium silicate hydrate hybrids: a new approach to cement based nanocomposites{

Novel hybrid organic-inorganic calcium silicate hydrate (C-S-H) materials have been synthesized via a sol-gel process. The materials are obtained by precipitation in alkali media of a mixture of trialkoxysilane (ethyltriethoxysilane, n-butyltrimethoxysilane or 3-aminopropyltriethoxysilane) and tetraethoxysilane diluted in CaCl 2 ethanol-water solution. XRD experiments show an increase of the basal distance of C-S-H with the content of trialkoxysilane suggesting the incorporation of organic moieties in the interlayer. 29 Si NMR data show that the organic species do not disrupt the inorganic framework of C-S-H. 2D 1 H-29 Si HETCOR NMR experiments confirm that trialkoxysilanes such as ethyl-or aminopropyl-silane are incorporated in the silicate chains of the C-S-H structure. In the case of highly hydrophobic trialkoxysilanes such as n-butyltrimethoxysilane, the results suggest that a separation occurs between silicates and trialkoxysilanes, leading to a mixture of inorganic C-S-H on one hand and 100% organosilane calcium hybrid phase on the other hand.

Introduction

Improvements in mechanical properties of hardened cements, mortars and concretes, such as toughness and strength, are a permanent challenge which could be successfully achieved by the incorporation of polymers as observed in hard biological materials. For example, cement-polymer composites with a flexural strength 10 times greater than that of ordinary cement pastes have been obtained by mixing high-alumina cements with water containing a water soluble polymer such as polyvinyl alcohol. 1,2 However, only a limited increase in fracture resistance has been achieved. This is probably due to bad dispersion and adhesive properties control of the organic component into the inorganic matrix which is thought to be the key to a biological material's fracture toughness. 3,4 In ordinary cement, the major phase responsible for cohesion is a calcium silicate hydrate labelled ''C-S-H''. C-S-H is thought to form nano-sized layered crystals with a structure related to tobermorite, 5 a natural mineral analogue. [START_REF] Taylor | Cement Chemistry[END_REF] The structure of the tobermorite layer is based on a distorted central Ca-O sheet flanked on each side by silicate ''dreierketten'' chains, i.e. silicate chains with a period of three tetrahedra. The interlayer space contains water molecules and additional Ca 2+ cations which balance the negative charge of the main layer. Compared to tobermorite, C-S-H can accommodate a substantial concentration of structural defects such as the omission of tetrahedra and a variable amount of calcium ions in the interlayer. [START_REF] Chen | [END_REF]8 Inconsistent results have been reported about the intercalation of polymers in C-S-H. Matsuyama and Young [9][10][11] claimed that a broad range of anionic, cationic and neutral polymers can be intercalated in the C-S-H layers depending on the Ca/Si ratio of the solid. This is in disagreement with further reports in which evidence of interlayer intercalation of previously used polymers was not found. 12,13 Herein, we propose an alternative method to incorporate small organic groups inside the interlayer. These organic groups are covalently linked to the inorganic part of C-S-H via Si-C bonds. Such hybrids are obtained by co-precipitation of R9Si(OR) 3 organotrialkoxysilane and Si(OC 2 H 5 ) 4 tetraethoxysilane (TEOS) mixtures in the presence of calcium salt in aqueous/ethanolic basic solution. Using this sample procedure, we were able to synthesize organic-calcium silicate layered hybrids with a large range of composition. The present work demonstrates that C-S-H can accommodate small sized organic groups in the interlayer space, directly linked to silicate chains.

Experimental

Calcium silicate hydrate-organic hybrid materials were prepared by the hydrolysis of a mixture of 5 6 10 23 mol of trialkoxysilane and tetraethoxysilane (TEOS) in a solution containing 0.46 g of CaCl 2 dissolved in 0.1 M HCl (1.35 cm 3 ) and ethanol (6.9 cm 3 ) for 1 h. 1 cm 3 of aqueous sodium hydroxide solution (4 M) was then added under vigorous agitation. The base addition resulted in a rapid precipitation. The precipitate was aged under agitation for 12 h at room temperature and then washed twice by centrifugation with deionized water. Finally, the solid was dried under vacuum for 1 day. The trialkoxysilanes used were ethyltriethoxysilane (C 2 H 5 Si(OC 2 H 5 ) 3 ), n-butyltrimethoxysilane (C 4 H 9 Si(OCH 3 ) 3 ), or 3-aminopropyltriethoxysilane (H 2 N(CH 2 ) 3 Si(OC 2 H 5 ) 3 ). In all samples, the Ca/Si molar ratio was fixed to 0.83 which is the typical ratio value found in well-crystallized calcium silicate hydrate (C-S-H). [START_REF] Taylor | Cement Chemistry[END_REF] The percentage of trialkoxysilane relative to the total source of silicon was varied from 0 to 100%. Elemental analysis of the synthesized hybrid equilibrium solutions showed that the great majority of silicon and calcium ions were incorporated into the solid phase.

X-Ray diffraction patterns were recorded using a powder diffraction set up in transmission mode 14 (Debye-Scherrer geometry) with Cu Ka 1 radiation (30 mA 6 30 kV) in 0.5 mm Lindemann capillaries for 5 h. NMR experiments were performed with a Bruker ASX 500 spectrometer. Crosspolarization magic angle spinning (CP-MAS) 29 Si and 2D 1 H-29 Si HETeronuclear chemical shift CORrelation (HETCOR) spectra were recorded using a MAS frequency of 5 kHz, a contact time of 2 ms and a recycle delay of 5 s. 1 H and 29 Si chemical shifts were referenced using adamantane and TMS respectively.

Results and discussion

Organic-calcium silicate hybrids were synthesized by varying the starting fraction of trialkoxysilane from 0 to 100% with a fixed Ca/Si ratio. The pure inorganic material (0% of trialkoxysilane, 100% of TEOS) XRD pattern is typical of a pattern of relatively well crystallized C-S-H 6 (Fig. 1). The pattern shows broad in-plane peaks at 0.304, 0.280, 0.182, and 0.167 nm together with a basal reflection at 1.25 nm. Resonances at 279 and 284 ppm are observed by 29 Si CP-MAS and they are assigned to end-chain tetrahedra Q 1 and middle-chain tetrahedra Q 2 species respectively 15 (Fig. 1).

At the opposite end of the series, the 100% organic-calcium silicate hybrids (100% of trialkoxysilane, 0% of TEOS) are characteristic of lamellar phases with a varying basal distance depending on the organic moiety size. In a previous study, we have proposed a structural model for these materials. 16 In the case of n-alkyltrialkoxysilanes used as starting precursors, XRD data are consistent with the presence of a bilayer alkyl chain arrangement between inorganic sheets. As observed in Fig. 2, the basal distance increases from 1.35 to 1.70 nm in lamellar hybrids made from ethyltriethoxysilane and n-butyltrimethoxysilane used as starting precursors respectively. The substitution of n-butyl by aminopropyl moieties linked to silane, which are approximately equal in size, leads to a similar basal distance value. In 100% organic-calcium silicate hybrid materials, broad in-plane diffraction peaks at 0.30 and 0.178 nm are always present. These peaks are consistent with those of a smectite-like layer arrangement, suggesting that the hybrid's inorganic part is characterized by silicon atoms condensed onto each side of a single Ca(OH) 2 brucite-like layer instead of the very distorted Ca-O layer of C-S-H. 16 Starting from pure inorganic end-series (C-S-H) and substituting a fraction of TEOS by trialkoxysilane, we observed a basal distance increase and progressive changes of the C-S-H non-basal peaks toward ill-defined peaks of the 100% organic calcium silicate hybrids (Fig. 2). In the case of the R9 = ethyl series, d 001 increased up to 1.47 nm for samples containing 40% ethylsilane precursor and then decreased to 1.35 nm for the 100% substituted material. This trend is highly pronounced in the case of the aminopropyl series. In this series, the basal distance reached a value of 2.14 nm for 60% substitution compared to 1.73 nm for the 100% substituted sample. In the 80% substituted sample, the layered arrangement was lost. The non-monotonic variation of d 001 with the percentage of trialkoxysilane for both series is not clear but seems inconsistent with a phase separation between C-S-H and 100% organic calcium silicate.

The XRD patterns of the n-butyl series (and longer n-alkyl series, see ESI{) are very different from the previous ones. As soon as the TEOS substitution became higher than 20%, the basal distance reached a constant value equal to that of the completely substituted sample. Moreover, the intensity of the basal peak increased with the substitution level. This suggests that for this series, a phase separation between the 100% organic calcium silicate phase and C-S-H probably occurs as soon as TEOS is substituted by n-butyltrialkoxysilane.

Additional structural information on the hybrid has been obtained by 29 Si NMR experiments (Fig. 3). As TEOS is progressively substituted by an organotrialkoxysilane, resonances in the chemical shift range of trifunctional silicon 17,18 (T n = RSi(OR9) 32n (OSi) n ) are observed at the expense of Q resonances. In substituted samples, the SiO 4 tetrahedra remain in the form of chains (Q 1 plus Q 2 environment) as in pure C-S-H. One can see however a broadening of these resonances when the fraction of TEOS decreases beyond 40%.

Organosilicate species are also condensed into the form of chains because only T 1 (d y 250 ppm) and T 2 (d y 260 ppm) resonances are present. In the ethyl and aminopropyl series, the T 2 /T 1 intensity ratio increases with the percentage of trialkoxysilanes. In these series, for samples containing a small fraction of organics (up to 40%), the predominantly T 1 environment for the trialkoxysilane species suggests either the formation of T 1 -T 1 or T 1 -Q 1 dimers or their incorporation at the end of the silicate chains (T 1 -Q 2 ). As the percentage of trialkoxysilanes increases, substitution of Q 2 by T 2 species occurs.

The behaviour of the n-butyl series is different because the shape of the T resonances does not evolve mainly with the butylsilane content{. This may imply that a large part of the trialkoxysilanes is separated into a phase similar to the 100% end-series hybrid. This assertion is supported by XRD results which have shown that the basal distance value of the 100% substituted phase is seen all along the series. The differences between ethyl, aminopropyl, and butyl (or longer n-alkyl) series may be assigned to the difference of the organic group chemical affinity toward the inorganic framework. Whereas small amounts of ethylsilane and aminopropylsilane groups may be diluted inside the interlayer of C-S-H which is hydrophilic in essence, the butylsilane species would rather cluster together due to strong hydrophobic interaction of the butyl groups, hence leading to separation.

To confirm the incorporation of small organic groups in C-S-H via co-precipitation of TEOS and triethoxysilane in the presence of the calcium salt, CP-MAS 29 Si and 2D 1 H-29 Si heteronuclear chemical shift correlation (HETCOR) experiments were performed on deuterated ethyl calcium silicate hybrids. In a first set of experiments, the materials were synthesized in D 2 O (without ethanol) using a NaOD solution to precipitate the hybrids which were then rinsed in D 2 O. The 1 H NMR experiments confirmed that almost all protons come from organic groups linked to silanes. In particular, it was verified, using 13 C CP-MAS NMR, that the alcohol formed by hydrolysis of the alkoxysilanes was eliminated during drying and that all alkoxy groups were hydrolyzed (see ESI{). The CP-MAS 29 Si of the deuterated ethyl calcium silicate hybrids showed intense Q 1 and Q 2 peaks whereas these resonances were not detectable in deuterated pure inorganic C-S-H (Fig. 4). We can then argue that only the protons of the ethyl groups are responsible for the polarisation transfer. This result is a direct proof that ethyl groups are spatially close to silicate tetrahedra and confirms that organotrialkoxysilanes are incorporated into the silicate chains. For the HETCOR experiments, samples were deuterated by exchange in D 2 O for 3 weeks before drying in order to reduce the contribution of protons from residual water and silanols compared to protons from organic moieties. In the HETCOR spectrum of the 40% ethyl calcium silicate hybrid (Fig. 5), the 1 H projection consists of a main resonance at 1 ppm due to ethyl groups as well as a small resonance at y5 ppm due to water molecules and Si-OH. The 2D 1 H slices clearly show that both Q 1 and Q 2 We can see that T species are slightly polarized by protons at 5 ppm suggesting a poor environment of water and silanols around ethyl groups. Both sets of NMR experiments ( 29 Si CP-MAS and 1 H-29 Si HETCOR) confirmed that organotrialkoxysilanes can be integrated in the silicate chains of C-S-H (identical conclusions have been obtained for aminopropyl calcium silicate hybrids, see ESI{). From these results, it may be possible to make a schematic representation of the hybrids containing a small fraction of organics as shown in Fig. 6. In this figure, the parent layered structure of C-S-H is represented in agreement with XRD and the Q region of 29 Si NMR which shows the C-S-H structure integrity for hybrid materials containing less than 40% of ethyl or aminopropyl silanes. Some silicate tetrahedra (Q species) have been replaced by trialkoxysilane (T species) leading to the incorporation of organic moieties in the interlayer.

Conclusion

In this work, we have shown that incorporating organic groups in calcium silicate hydrates (C-S-H) can be achieved by coprecipitation of TEOS and trialkoxysilane with calcium salt in alkali media at room temperature without disrupting the inorganic framework. Whereas the present results have been obtained for very small organic groups, it seems however that phase separation occurred for larger-sized or for highly hydrophobic organic groups. This simple approach may offer new opportunities to design new cement based nanocomposites by covalently bonding monomer molecules into the interlayer of C-S-H and then proceeding to in-situ polymerisation. An alternative method currently under study consists of precipitating C-S-H in the presence of polymers containing trialkoxysilane functions in order to induce covalent bonding between the polymer and the silicate phases.

Fig. 1

 1 Fig. 1 XRD diagram (a) and 29 Si CP-MAS NMR spectrum (b) of C-S-H (without trialkoxysilane). XRD peaks have been indexed in an orthorhombic cell with a = 0.560 nm, b = 0.364 nm, and c = 1.25 nm as suggested by Taylor. 6

Fig. 2

 2 Fig. 2 XRD diagrams of (a) ethyl, (b) n-butyl, and (c) aminopropyl calcium silicate hybrid series. In each figure, the fraction of trialkoxysilane (in mol%) varies from 20 to 100% by 20% steps from the bottom to the top. (d) Basal distance variation (d 001 ) with trialkoxysilane percentage for ethyl, butyl, and aminopropyl organic moieties.

Fig. 3

 3 Fig. 3 29 Si CP-MAS NMR spectra of (a) ethyl, (b) n-butyl, and (c) aminopropyl calcium silicate series. In each figure, the fraction of trialkoxysilane (in mol%) varies from 20 to 100% by 20% steps from the bottom to the top. Due to non-quantitative results obtained by cross-polarization, the T/Q ratio does not actually match the initial organotriethoxysilane/TEOS molar ratio (* denotes spinning sidebands). { Similar trends are observed with n-octylsilane, see ESI.{

Fig. 4

 4 Fig. 4 29 Si CP-MAS NMR spectra of deuterated samples made on 100% TEOS (pure C-S-H) and made on a mixture of 40% ethyltriethoxysilane and 60% TEOS. The intensities have been normalized by the sample mass and the scan number. The only proton source needed for polarization transfer came from ethyl groups linked to silicon atoms. The large Q 1 and Q 2 signals, only visible in the hybrid sample, are evidence of the homogeneous distribution of organic moieties in the 40% ethyl calcium silicate material.

Fig. 5

 5 Fig. 5 2D 1 H-29 Si heteronuclear chemical shift correlation (HETCOR) NMR experiments recorded for a sample containing 40% ethyltriethoxysilane and 60% TEOS. The 1 H MAS projection is shown along the vertical axis. This figure shows strong correlation peaks between protons of ethyl groups and Q species.

Fig. 6

 6 Fig. 6 Schematic model of a layer of hybrid calcium silicate materials showing the incorporation of organotriethoxysilane at the end and in the middle of C-S-H silicate chains. (N = calcium atoms).