Hiram Calvo
email: hcalvo@cic.ipn.mx

Ángel Hernández-Castañeda

Jorge García-Flores

Author Identification using Latent Dirichlet Allocation *

We tackle the task of author identification at PAN 2015 through a Latent Dirichlet Allocation (LDA) model. By using this method, we take into account the vocabulary and context of words at the same time, and after a statistical process find to what extent the relations between words are given in each document; processing a set of documents by LDA returns a set of distributions of topics. Each distribution can be seen as a vector of features and a fingerprint of each document within the collection. We used then a Naïve Bayes classifier on the obtained patterns with different performances. We obtained state-of-the-art performance for English, overtaking the best FS score reported in PAN 2015, while obtaining mixed results for other languages.

Introduction

Author verification is an important problem to solve since many tasks require recognizing the author who wrote a specific text. For example, from knowing which author wrote an anonymous book, up to identifying notes of a serial killer. In this paper we deal with an author verification challenge from a more realistic approach. Specifically, the dataset used consists of one to five documents of a known author and one document of an unknown author. The corpus is formed by four subsets in different languages (English, Spanish, Dutch and Greek). The aim is to identify whether a written unknown text was written by the same author who wrote the known texts. It is important to note that this task becomes more difficult when the dataset is composed of short documents; since current approaches are not able to capture effective models with few amounts of words [START_REF] Stamatatos | A survey of modern authorship attribution methods[END_REF]. However, on real cases as forensic field, long texts rarely exist.

Several approaches have been conducted to generate more informative features based on text style. Nevertheless, it is also possible to generate features by extracting lexical, syntactic, semantic information among others. Lexical information is limited to word counts and occurrence of common words. On the other hand, syntactic information is able to obtain, to a certain extent, the context of the words.

In this work we use semantic information to find features that help us to discriminate texts. For this purpose, we create a model by using Latent Dirichlet Allocation (LDA). By using this method, we consider all the vocabulary from all texts at the same time, and, after a statistical process, find to which extent the relations between words are given in each document. LDA is a statistical algorithm which considers a text collection as a topics mixture; then, processing a set of documents by LDA returns a set of topic distributions. Each distribution can be seen as a vector of features and a fingerprint of each document within the collection. We use machine learning algorithms to classify the obtained patterns.

In this work we obtained the following F-measures: 85.5% for English, 76.0% for Spanish, 70.9% for Dutch and 64.0% for Greek.

Related work

Several works have attempted the authorship identification challenge by generating different kinds of features [START_REF] Narayanan | On the feasibility of internet-scale author identification[END_REF], [START_REF] Madigan | Author identification on the large scale[END_REF]. The nature of the dataset can determine the difficulty of the task, i.e., how hard will be to extract appropriate features [START_REF] Green | Comparing Frequency-and Style-Based Features for Twitter Author Identification[END_REF], [START_REF] Afroz | Detecting hoaxes, frauds, and deception in writing style online[END_REF]. In [START_REF] Nirkhi | Comparative study of authorship identification techniques for cyber forensics analysis[END_REF] can be seen that, while the number of authors increases and the size of training dataset decreases, classification performance lowers. This sounds logical since, when the size of training data is lower, the identification of helpful features becomes affected.

Many works address author identification through the author's writing style [START_REF] Pateriya | A Study on Author Identification through Stylometry[END_REF], [START_REF] Pavelec | Author identification using stylometric features[END_REF]. For instance, in [START_REF] Layton | Local n-grams for Author Identification[END_REF], style-based features are compared to the BoW (Bag of Words) method. This study attempts to discriminate authors from texts in the same domain obtained from Twitter. Style markers such as characters, long words, whitespaces, punctuation, hyperlinks, parts of speech, among others, were included. The study findings showed that a style-based approach was more informative than a BoW-based method. However, their best results were obtained when considering two authors, so there was an accuracy decrease when the number of authors was increased. This suggests that, depending on how big is the training set, there will be stylistic features that help to distinguish an author from other, but not from all other authors.

Stylistic features also can be applied to other tasks. In [START_REF] Bergsma | Stylometric analysis of scientific articles[END_REF], the authors combined features to address two-class problems. This work attempts to obtain style, BoW and syntax features to classify native and non-native English, texts written for conference or workshop and texts written by male or female. The dataset consists of scientific articles. This kind of texts is more extensive, compared to e-mail, tweets, or other short texts; this could have led to identify non-native written texts with promising accuracy. Nevertheless, long texts not necessarily ensure good results, since classification tasks on venue and gender obtained low accuracy.

The purpose of identifying authorship can vary. For example, Bradley et al. [START_REF] Bradley | Author identification from citations[END_REF] attempt to prove that it is possible to find out which author wrote an unpublished paper (for a conference or journal); they consider only the cited works in them. By using LSA, the authors propose to create a term-document matrix wherein possible authors are considered as documents and authors who are cited are considered as terms. The results of Bradley et al. showed that the blind review system should be examined in greater detail. Another example is the Castro and Lindauer's work [START_REF] Castro | Author Identification on Twitter[END_REF], with the task of finding out whether Twitter users identity can be uncovered by their writing style. The authors focused in features such as word shape, word length, character frequencies, stop words' frequencies, among others. With an RLSC (Regularized Least Square Classification) algorithm, the authors correctly classified 41% of the tweets.

In the work of Pimas et al. [START_REF] Pimas | Know-Center at PAN 2015 author identification[END_REF], the author verification task is addressed by generating three types of features. The authors extract stylometric, grammatical and statistical features. Our This study is based on PAN 2015 authorship verification challenge. In addition, Pimas et al consider topics distribution as well, but they argue against using it, because the dataset is formed by topic mixtures. A cross validation model (10 folds) shows good performance, but, on the other hand, the model got overfitting using the training and test sets specified in the dataset.

Author verification

In this section we present our method for author verification. First, in Section Error! Reference source not found. we detail the source of features we use. Next, in Section 3.2 we describe the dataset used in this work for evaluation, and finally in Section 3.3 we give details on our feature vector construction.

Latent Dirichlet Allocation (LDA)

LDA [START_REF] Blei | Latent dirichlet allocation[END_REF] is a probabilistic generative model for discrete data collections such as texts collection. It represents documents as a mix of different topics. Each topic consists of a set of words that keep some link between them. Words, in its turn, can be chosen based on probability. The model assumes that each document is formed word-byword by randomly selecting a topic and a word for this topic. As a result, each document can combine different topics. Namely, simplifying things somewhat, the generation process assumed by the LDA consists of the following steps:

1. Determine the number N of words in the document according to the Poisson distribution. 2. Choose a mix of topics for the document according to Dirichlet distribution, out of a fix set of K topics. 3. Generate each word in the document as follows:

a) choose a topic; b) choose a word in this topic. Assuming this generative model, LDA analyzes the set of documents to reverseengineering this process by finding the most likely set of topics of which a document may consist. LDA generates the groups of words (topics) automatically; see Figure 1.

Accordingly, LDA can infer, given a fixed number of topics, how likely is that each topic (set of words) appear in a specific document of a collection. For example, in a collection of documents and 5 latent topics generated with the LDA algorithm, each document would have different distributions of 5 likely topics. That also means that vectors of 5 features would be created.

Dataset

To conduct experiments with our approach, we use the corpus proposed in the author identification task of PAN 2015 [START_REF] Stamatatos | Overview of the Author Identification Task at PAN 2015[END_REF]. The dataset consists of four subsets, each set written in different languages: English, Spanish, Dutch and Greek. Subsets have significant differences. The English subset consists of dialog lines from plays; the Spanish subset consists of opinion articles of online newspapers, magazines, blogs and literary essays; the Dutch subset is formed by essays and reviews; and the Greek subset is formed by opinion articles of categories as politics, health, sports among others. The corpus also has different number of documents per subset detailed in Table 1. In addition, each language consists of several problems to solve which are specifically defined below (Section 3.3).

Due to its nature, this dataset focused on problems which require capturing more specific information about the writing style of the author. For example, suppose we know a person who worked for a newspaper, writing articles about sports; but one day, this person decides to be independent and spend her life writing horror novels. One possible task can be to find out which articles belong to the sport ex-writer among sport articles of different authors-in this case, the vocabulary of the documents can uncover the author; for instance, by her usage rate of n-grams as features. On the other hand, another possible task is to discover whether a horror novel was written by the novelist, based on the sport articles which she wrote before., This is a drastic change in genre and topic of the documents, i.e., the intersection between vocabularies of the documents would be substantially reduced.

Method

As an attempt to overcome this problem, we propose to use Latent Dirichlet Allocation (LDA) for extracting semantic information of the corpus. As mentioned before, given a collection of texts, LDA is able to find relations between words by their position in the text. Common stylistics approaches try to find discriminating symbols in the documents so they can distinguish between two documents written by different authors; however, as we stated before (Section 2), while texts become shorter, the amount of symbols is not enough to produce effective discriminate features. This fact becomes worse when authors number is increased. In the case of LDA we expect this issue to be less problematic.

We infer that writers have different ways to link words due to the fact that each writer makes use of favorite phrases. For example, some author usually may use the phrase "the data gathered in the study suggests that" in contrast to other author who uses "the data appears to suggest that". Thus, the words "the, in, to, that" can be included in different topics since, unlike LSA [START_REF] Dumais | Latent semantic analysis[END_REF]. LDA can assign the same word to different topics as an attempt to better handle polysemy. As a result, to use several words at different rates shall result in different topic distributions for each document.

The task of the dataset used for this study is as follows. For each language or subset of the dataset there are specific number of problems; for each problem in turn there are from one to five documents considered as known and one document considered as unknown. These known documents are written by the same author. To solve a specific problem, we must find out whether the unknown document was written by the same author which writes the known documents.

To represent each problem, all documents in the dataset are processed with LDA. Then, we obtain vectors (with real values -probability of each topic) which represent known and unknown documents. Based on a specific problem, we do a subtraction between each known-document's vector and the unknown-document's vector (let us remember that there is only one unknown document by problem, however there are from one to five known documents). We found that converting real values to {0, 1} values slightly improved final results, so we used the arithmetic mean as threshold; 0 represents topic absence and 1 topic presence (above a certain threshold). Therefore, the subtraction between vectors can result in two possible values: 0 when topics are equal and 1 when topics are different (See Figure 2).

Results

In the following experiments, we use a Naïve Bayes classifier for classification. For all experiments we chose the number of topics to be 3. Therefore, patterns of three features were generated by each document. We found that varying the topics number, changed the performance classification. There is not a method for determining how many topics we should to choose for incrementing performance. Thus, we had to fix an interval until we achieved the best results. We show in Table 2 results of performance measures (explained below) regarding the number of topics selected. This table shows that the best results are around 3 topics. Interestingly, with vectors with only a few of topics, we obtained over 64% accuracy. Actually, one might suppose that documents could have been categorized by subject; however, that assumption is unlikely because, as we showed in Section 3.2, the dataset used is formed of topics and genres mixtures. We conducted two experiments for knowing whether two documents written by the same author will be similar on their distribution of topics. Figure 3 shows the sum of all differences by topic in the test dataset for English. As we can see, the amount of differences is high when texts are written by different authors. In Figure 4 is also showed that differences for Spanish language.

We classified the dataset without pre-processing and show in Table 3 the following values: Accuracy, F-measure (F), Precision (P), Recall (R). While accuracy is a measure used in many works on deception detection and it provides us a point of comparison with other results, we also opted for showing precision, recall, and Fmeasure; this allows for a deeper analysis of outputs. Thus, precision shows the percentage of selected texts that are correct, while recall shows the percentage of correct texts that are selected. Finally, F-measure is the combined measure to assess the P/R trade-off.

We classified the dataset without pre-processing and show in Table 3 the following values: Accuracy, F-measure (F), Precision (P), Recall (R). While accuracy is a measure used in many works on deception detection and it provides us a point of comparison with other results, we also opted for showing precision, recall, and Fmeasure; this allows for a deeper analysis of outputs. Thus, precision shows the percentage of selected texts that are correct, while recall shows the percentage of correct texts that are selected. Finally, F-measure is the combined measure to assess the P/R trade-off. We obtained the best result for English subset with 85.6% accuracy even when it has the biggest training set (500 problems) of the corpus. Spanish subset ranks second with 76.0% accuracy, Dutch subset reached 70.9% accuracy and finally Greek subset reached 64.0% accuracy. Both English and Greek subsets obtained the first and the last place of the results (Table 3) respectively; therefore, we cannot infer that the topics mixture made the difference in results since both subsets consist of themes mixture and one of them was not affected. Similarly, for both Spanish and Dutch

Different author

The same author subsets (second and third place respectively), results did not lead to conclude that the genre mixture had some correlation on it. For these reasons, we consider that the results were directly affected by the training and test set's document selection and not by the type of text. We compare our results with those obtained in author identification task at PAN 2015 evaluation lab [START_REF] Stamatatos | Overview of the Author Identification Task at PAN 2015[END_REF]. Therefore, we calculated, as PAN-2015 task's authors, a final score which is the product of two values: c@1 [START_REF] Peñas | A simple measure to assess non-response[END_REF] and area under the ROC curve (AUC) [START_REF] Fawcett | An introduction to ROC analysis[END_REF]. The former is an extension of the accuracy metric and the latter is a measure of classification performance which provides more robust results than accuracy.

We show in Table 4 the better results obtained for each language subset by participants of PAN-2015 task. According to those results, our method seems to perform well for both English and Dutch languages. This work outperforms FS results with regard to English subset and had better performance than Bartoli et al. and Bagnall's result with regard to Dutch subset. On the other hand, for both Spanish and Greek subsets the proposed method did not show good performance however, ROC curve results showed that predictions are acceptable.

Conclusions

A common approach to verify authorship is attempting to find the author's writing style. Therefore, the assumption is that by using that approach, it is possible to capture specific features to discriminate one author from others. This hypothesis is hard to prove; nevertheless, it is known that certain amount of data is necessary to find more appropriate features leading to high classification performance. Data is a problem, for instance, for the forensic field, since hardly there are long texts and they are in different domains. We showed in this work how LDA responds to verify authorship when there is limited data; i.e., only from one to five short texts written by a specific author to determine whether an unknown document belongs to the same author. Furthermore, the used datasets consist of topic and genre mixtures. Basically, we used documents distributions to capture what we call the authors' fingerprint. Then, by subtraction between topic distributions, we found that documents written by different author tend to differ more than those written by the same author. This approach allowed us to achieve 74% accuracy on average.

Figure 1 .

 1 Figure 1. Example of generated topics by using LDA.

Figure 2 .

 2 Figure 2. Example of subtraction between known-document's vector and unknowndocument's vector

Figure 3 .Figure 4 .

 34 Figure 3. Topic differences between document written either the same or different author (English subset)

Table 1 . Specific values for dataset of author identification task 2015

 1

		Training problems		Test problems
	Language	Items	# docs	Avg. words x doc.	Items	# docs	Avg. words x doc.	Kind
	English	100	200	366	500	452	536	Cross-topic
	Spanish	100	500	954	100	1000	946	Cross-topic/genre
	Dutch	100	276	354	165	380	360	Cross-genre
	Greek	100	100	678	100	500	756	Cross-topic

Table 2 .

 2 Selection of topics number based on PAN-2015's author identification task measures

	No. Topics	c@1	AUC	FS
	2	0.228	0.228	0.052
	3	0.856	0.807	0.691
	4	0.702	0.908	0.637
	5	0.774	0.863	0.668
	6	0.660	0.695	0.459
	7	0.770	0.806	0.621
	8	0.684	0.797	0.545
	9	0.730	0.753	0.550
	10	0.711	0.834	0.593
	20	0.488	0.503	0.245
	40	0.496	0.505	0.250
	60	0.496	0.468	0.232
	80	0.524	0.568	0.298
	100	0.468	0.497	0.233

Table 3 . Results of each subset classification

 3

	Subset	Accuracy (%)	Precision	Recall	F-measure
	English	85.6	0.864	0.856	0.855
	Spanish	76.0	0.760	0.760	0.760
	Dutch	70.9	0.733	0.709	0.702
	Greek	64.0	0.646	0.640	0.640

Table 4 . Results comparison with other authors. FS=c@1*AUC.

 4

	Author	Measure	English	Subset Spanish	Dutch	Greek
		c@1	0.757	0.814	0.644	0.851
	Bagnall 2015	AUC	0.811	0.886	0.700	0.882
		FS	0.614	0.721	0.451	0.750
		c@1	0.559	0.830	0.689	0.657
	Bartoli et al. 2015	AUC	0.578	0.932	0.751	0.698
		FS	0.323	0.773	0.518	0.458
		c@1	0.638	0.755	0.770	0.781
	Moreau et al. 2015	AUC	0.709	0.853	0.825	0.887
		FS	0.453	0.661	0.635	0.693
		c@1	0.856	0.760	0.709	0.640
	This work	AUC	0.808	0.737	0.785	0.688
		FS	0.692	0.560	0.556	0.440

* The authors wish to thank the support of the Instituto Politécnico Nacional, (COFAA, SIP) and the Mexican Government (CONACYT, SNI).


Romeo Giuliano
email: r.giuliano@unimarconi.it

Anna Maria Vegni
email: annamaria.vegni@uniroma3.it

Valeria Loscrí
email: valeria.loscri@inria.fr

Eros Innocenti

Alessandro Vizzarri
email: alessandro.vizzarri@uniroma2.it

Franco Mazzenga
email: mazzenga@ing.uniroma2.it

MuSLi: a Multi Sensor LiDAR Detection for C-V2X Networks

Keywords:

Obstacle detection is a tool adopted in vehicular safety applications, aiming to detect a moving obstacle in an area of interest with the highest accuracy. Different sensors are used for this aim, such as LiDAR devices mounted on board of a vehicle that capture images of the surrounding environment. Extending the number of LiDAR sensors can be useful to improve the obstacle detection accuracy, since multiple images are captured from different distances and directions, and this represents an interesting approach, specially in case of dense networks with cooperative nodes. In this paper we present MuSLi technique, aiming to (i ) provide an accurate obstacle detection and (ii ) forward alert messages to other cars in the network, in case of correct detection of a pedestrian crossing the street. MuSLi relies on the connected content islands scenario, where each vehicle defined as a content island subscribes to a service in order to receive and share published messages. Specifically, the road safety service allows the detection of an obstacle through multiple LiDAR sensors from neighboring cars. Furthermore, we investigate the fastest transmission mode among those defined in the C-V2X releases to alert the presence of a pedestrian to the other approaching cars. The proposed technique provides the distances by the crossroad in which is better to use V2V, V2I or V2N according to the environment, the scheduling technique and the measured interference.

Machine Learning, Multi-sensor detection.

Introduction

In the near future, it is envisioned that with 6G we will assist at a new era of billions of things, drones, robots, people and vehicles connected to each other and generating Zettabytes of digital information [START_REF] Liu | Vision, requirements and network architecture of 6g mobile network beyond 2030[END_REF]. In this context, novel services and applications will be more challenging, and enriched with immersive communication and characterized with more stringent requirements. In this sense, 6G is regarded as the key future generation network, able to tackle these challenges, by encompassing both human and machine-centric philosophy [START_REF] Zhu | Exploring the road to 6g: Abc ? foundation for intelligent mobile networks[END_REF].

In the vehicular context, 6G is regarded as the technology allowing the development of simultaneous localization and mapping (SLAM) approaches, for enabling advanced cross reality applications encompassing autonomous vehicles and drones. In [START_REF] Huang | A survey of simultaneous localization and mapping with an envision in 6g wireless networks[END_REF], Huang et al. provide a detailed survey on SLAM. In particular, they consider Light Detection And Ranging (LiDAR) and Visual SLAM and describe the devices, their evolution, the main challenges and finally provide a constructive discussion on future directions. With the deployment of 5G around the world, research community and industry have already started to investigate the 6G network, and specifically the concept of network intelligentization, that will replace the virtualization concept that characterized 5G networks [START_REF] Letaief | The roadmap to 6g: Ai empowered wireless networks[END_REF]. Intelligentization is meant as connected intelligence with massive use of Machine Learning (ML) capability for enabling the capacity of 6G systems to embrace new emerging technologies, such as THz and intelligent reprogrammable surfaces, thus enabling a reprogrammable environment paradigm. The key factors in such a kind of complex environment are represented by the massive use of Artificial Intelligence (AI) and the high heterogeneity and complexity of the 6G systems, where the softwarization concept, featured in 5G networks, will not be enough to support the new requirements and applications [START_REF] Tang | Future intelligent and secure vehicular network toward 6g: Machine-learning approaches[END_REF].

In the context of Internet of Vehicles (IoV) paradigm, as part of the Intelligent Transport System (ITS) and with the advent of autonomous vehicles, one of the most important functionality to be developed is the obstacle detection [START_REF] Maru | Obstacle detection for vehicles in intelligent transport system[END_REF][START_REF]Obstacle detection and track detection in autonomous cars[END_REF]. In order to develop advanced control systems for detecting obstacles, a combination of advanced sensors, radar and camera need to be considered for building an accurate environment perception. A first categorization of sensors/devices for obstacle detection considers cooperative and non-cooperative sensors, as described in [START_REF] Mukhtar | Vehicle detection techniques for collision avoidance systems: A review[END_REF]. Among the different cooperative sensors, we remind the Automatic Dependent Surveillance and Broadcasting (ADS-B) and the Traffic Alert Collision Avoidance System (TCAS), while as non-cooperative sensors, we have infrared, electro-optical, acoustic, LiDAR, sonar, radar, etc.

In [START_REF] Habermann | Obstacle detection and tracking using laser 2d[END_REF], Habermann and Garcia focus on laser sensors for realizing obstacle detection. LiDAR technology is introduced in [START_REF] Aijazi | Multi sensorial data fusion for efficient detection and tracking of road obstacles for inter-distance and anti-colision safety management[END_REF] for collision avoidance, and the authors apply a fusion technique to 3D LiDAR and 2D image data for realizing efficient anti-collision management. The LiDAR sensor and a camera sensor are exploited for obstacle detection; the fusion of the images is then realized, in order to improve the accuracy of the single image and an improvement of the latency is also obtained based on this fusion algorithm. The use of LiDAR for obstacle detection has been employed also in [START_REF] Han | Enhanced road boundary and obstacle detection using a downward-looking lidar sensor[END_REF], where line segments are extracted by processing raw data of the ranging sensor and downward-looking light detection. This approach allows a good distinction between line segments and obstacles under various road conditions. Anyway, in the most of papers from the literature, the analyzed sensors and the proposed fusion techniques foresee that sensors are mounted on board on the same vehicle, with the constraint to have a limited -single-vision of the investigated area. On the other side, by considering a networked technique where multiple nodes can cooperate in a joint manner for a common task, e.g. the obstacle detection, we expect to obtain an enhanced vision of the investigated area, and then the obstacle detection will be likely more accurate as compared to single sensor-based approaches.

Leveraging on current literature results, in this paper we aim to investigate a multi-sensor obstacle detection approach for fast and accurate detection, to be adopted in safety vehicular applications. Specifically, we adopt ML techniques for pedestrian detection in vehicular scenarios through the use of multiple Li-DAR sensors mounted on vehicles. The pedestrian detection task occurs through the collection of multiple signals coming from the available neighboring vehicles, that are approaching an area of interest e.g., a crossroad. The fusion of collected LiDAR images (i.e., clouds of points) is useful to increase the accuracy of obstacle detection; as a result, the probability of detection will result increased and more accurate.

Main contributions

In this paper, we present a multi sensor LiDAR detection technique, namely MuSLi, aiming to detect obstacles in a vehicular environment. We extend the "content island" model, as introduced in [START_REF] Patra | Leveraging a publish/subscribe fog system to provide collision warnings in vehicular networks[END_REF], so that both vehicles (i.e., cars 1 and Road Side Units (RSUs) are equipped with various sensors and data capture devices, and are able to exchange data to neighboring vehicles and also process them. Specifically, content islands are a set of interconnected vehicles and RSUs with sensing and processing capabilities, which exchange data using a publish/subscribe system. Exchanged messages can either contain data or an action trigger, which is a request originating from one vehicle/RSU soliciting approaching nodes to process a dataset or simply inquiring for a given query. For this aim, we can rely on the Message Queue Telemetry Transport (MQTT) protocol to provide the required publish/subscribe-based communication. MQTT is a widespread standard in Internet of Things (IoT) for publish/subscribe systems, and in the context of vehicular networks, it is being used in a wide range of applications such as to provide collision warnings [START_REF] Patra | Leveraging a publish/subscribe fog system to provide collision warnings in vehicular networks[END_REF], to design a traffic control system to minimize the response time of emergency vehicles [START_REF] Madisa | Android and cloud based traffic control system[END_REF], and for sharing energy between two electric vehicles [START_REF] Taghizadeh | Design and implementation of an advanced vehicle-to-vehicle (v2v) power transfer operation using communications[END_REF].

Thanks to the LiDAR sensors mounted on board, "content island" cars close to a crossroad are able to detect pedestrians crossing the street. They share their collected LiDAR clouds in order to perform a detection based on multiple point 1 The terms "vehicle" and "car" are used interchangeable in this paper.) clouds for the same pedestrians, and thus improving the detection accuracy.

As compared to single-sensor techniques, MuSLi will provide higher obstacle detection probability, reaching an enhancement of 35% of accuracy at a distance of 10 m from the area of interest. As expected, the higher the number of multiple LiDAR point clouds, the higher will be the detection probability.

Notice that MuSLi approach has been intended with the twofold goal of (i ) obstacle detection, and (ii ) alert message forwarding. Indeed, after detecting a pedestrian crossing the street, this information triggers a message forwarding task, where cars will disseminate alerting messages to other approaching cars through Cellular-Vehicle to everything (C-V2X) approach [START_REF] Bazzi | On the design of sidelink for cellular V2X: a literature review and outlook for future[END_REF]. Different V2X scenarios require the transport of messages with different performance requirements for the 3GPP system. In [START_REF]Service requirements for enhanced V2X scenarios (3GPP TS 22[END_REF], Extended Sensors is one of the six different use cases with 3GPP support for V2X scenarios. It enables the exchange of raw or processed data gathered through local sensors or live video data among cars, RSUs, devices of pedestrians and V2X application servers. In the proposed framework, cars approaching to a given area (e.g., a crossroad) may subscribe to the extended sensor service. They are able to publish the contents collected by their on-board sensors (i.e., the LiDAR clouds) and receive data from the other subscribed cars. As a result, the awareness of the road environment is enhanced due to data from multiple sensors. In case of obstacle detection e.g., a pedestrian crossing the crossroad, subscribers can send alarm messages to approaching vehicles. In this context, we also evaluate the delay in message alarm delivery by considering a dynamic and a semi-persistent scheduling strategies, for specific propagation environments such as urban, sub-urban and rural, transmission modes i.e., Line-Of-Sight (LoS) and Non-Line-Of-Sight (NLoS), and the amount of interference power in the considered area. The main objectives of this paper are as follows:

1. We introduce MuSLi technique, aiming to detect obstacles in vehicular environments. A source vehicle, defined also as content island, exploits the collection of multiple LiDAR point clouds coming from neighboring cars, close to an area of interest (e.g., a crossroad), which are able to detect an obstacle thanks to their LiDAR sensors installed on board. A pool of signals from multiple LiDAR sensors are collected and processed by means of a ML approach, for a more accurate obstacle detection, as compared to single-sensor approaches; 2. MuSLi is intended with the twofold aim of both (i ) obstacle detection and (ii ) alert message forwarding. The latter task is triggered by the previous one, in case of positive obstacle detection. Alert messages are then forwarded to approaching cars, by means of C-V2X. We will evaluate different communication strategies on some propagation environments and signal interference levels, in order to select the optimal communication mode that can guarantee the fastest message forwarding; 3. MuSLi runs at application level as in [START_REF]3GPP, Application layer support for Vehicle-to-Everything (V2X) services[END_REF] and allows data exchange among cars according to a typical distribution protocol such as MQTT. The performance assessment of MuSLi is carried out by means of simulation results expressed as both the accuracy of obstacle detection and the latency in transmitting alert messages via C-V2X. According to the environment, propagation conditions and distances from the approaching cars, we will discuss about the optimal transmission method to provide a reduced latency in message delivery. This paper is organized as follows. In Section 2 we present the system model adopted to represent mathematically the reference scenario. We detail the formulation of the MuSLi performance, expressed in terms of outage probability and latency in different communication and transmission modes. An optimization problem related to the latency on transmission links is also addressed. Our proposed MuSLi technique is presented in Section 3, where we first introduce how obstacle detection occurs through an ML approach, and then how to forward alert messages in case an obstacle is detected. A more detailed description of how MuSLi can detect an obstacle is presented in Section 4. The effectiveness of MuSLi is then assessed in Section 5 for different communication modes and vehicular scenarios. Finally, conclusions are drawn at the end of the paper.

System Model

A short description of the reference scenario and the different communication modes, providing different network performance, is introduced hereafter.

Reference Scenario

In the following, we report the scenario and the modeling of the reference system. We consider that one or more cars are close to a crossroad. Each car, defined as content island, is equipped with a LiDAR sensor able to scan the crossroad area and to detect possible pedestrians crossing the neighboring streets. Based on the detection performed by cars close to the crossroad, alerting messages are sent to other cars approaching the crossroad, with the aim of signaling the presence of pedestrians (hard to be seen by the drivers due to the distance), thus allowing them to properly adapt their speed. Message exchange and forwarding occur by means of a publish/subscribe system. For instance, a node can be subscribed to a service i.e., road safety, and receive a published message i.e., a warning alert related to obstacle detection, from another node in a given area. Similarly, a source node collects different information from neighboring cars i.e., LiDAR images, process them and finally publish the obtained result i.e., obstacle detection. The content island cars are based on the model introduced in [START_REF] Patra | Leveraging a publish/subscribe fog system to provide collision warnings in vehicular networks[END_REF][START_REF] Manzoni | A proposal for a publish/subscribe, disruption tolerant content island for fog computing[END_REF]. In this paper, we consider the island core is comprised of an MQTT broker, a Virtual Processing Client (VPC), and the multicast daemon, as well depicted in Figure 1. The MQTT broker is the element to which LiDAR sensor present inside a vehicle is connected. By connecting to the broker, it may publish information that is delivered to other cars having subscribed to such a service.

A VPC is a special client aims to control the flow of data information, while the groupcast daemon is a service used by the VPC to send and receive broadcast message from neighboring content island cars. For this purpose, VPC sets up MQTT messages to connect multiple content islands for data exchange.

After introducing the content island vehicle concept, in Figure 2 we report the elements envisaged in a "Connected Content Islands" scenario. In addition to the cars able to transmit messages among them at short distances, RSUs are present along the roads and able to directly communicate to the cars in proximity. Moreover, it is possible to provide communication among cars by using local deployed base stations (i.e., eNB and gNB). From Figure 2, the following vehicular communication modes are highlighted:

• Vehicle-to-Vehicle (V2V), in case the communication occurs between two On-Board Units (OBUs) mounted on the content island cars;

• Vehicle-to-Infrastructure (V2I), in case the communication occurs between an OBU and a content island RSU;

• Vehicle-to-Network (V2N), in case of communications among content island cars through a local base station over the cellular network, thus enlarging its reachable area. In this case, we observe the communication can occur through a Cloud server or a Mobile/Multi-access Edge Computing (MEC) node, which relay the message.

Notice that all these communication modes are possible for both LoS and NLoS transmission links, due to the presence of obstacles (e.g., buildings, trees, etc.) along the transmission range. In the next section, we will observe that the most appropriate communication mode depends on channel conditions and specific scenario characteristics that affect the signal propagation.

Evaluation Model

In order to assess the performance of the considered system, we evaluate the outage probability for different transmission modes both in downlink and uplink 2 , which indicates the probability that a message sent by a vehicle is correctly received by a destination node i.e.,

P out = Pr {SIN R < ρ 0 } , (1)
where ρ 0 is the Signal-to-Interference plus Noise (SINR) threshold for the considered service. In Eq. ( 1), the SINR parameter can be expressed as:

SIN R = P T • G T G R L(d) •   1 η • 1 + I η   , (2)
where P T [W] is the transmitting power, G T and G R are the antenna gains for the transmitter and the receiver respectively, L(d) is the pathloss accounting for 2 We assume that downlink and uplink are balanced.

Table 1: Main parameters adopted in different environments and communication modes. In Eq. ( 2), the expression of pathloss depends according to different scenarios and transmission modes. Specifically, from [START_REF][END_REF], we have considered three different environments i.e., (i ) Urban Micro, (UMi), (ii ) Urban Macro, (UMa), and (iii ) Rural, and the radio transmission in case of (i ) LoS and (ii ) NLoS.

V2V V2I V2N h veh = 1.5 m h BS = 4 m h BS = 10 m G T /R = 3 dB G T /R = 3 dB UMi UMa Rural G T /R = 10 dB G T /R = 15 dB G T /R =
Only the slow fading has been taken into account, since the small-scale fading is compensated in the Orthogonal Frequency-Division Multiplexing (OFDM) receiver by the one tap equalizer. Table 1 collects

Latency assessment

According to the schematic in Figure 3, we considered different vehicular communication modes i.e., V2V, V2I, and V2N, and for each of them, the communication involves different elements in the reference network. In V2V mode, the communication occurs directly between two neighboring cars in LoS, and no further element is considered (black arrow in Figure 3). In V2I, a vehicle approaching to a RSU transmits a message to it (i.e., blue arrow of V2I connectivity link), which relays it to another approaching vehicle (i.e., blue arrow of I2V connectivity link). Finally, in V2N mode a vehicle communicates with a local Multi-Access Edge Computing (MEC) or a remote Cloud server through the local eNB/gNB, and then the message will be redirected toward a destination vehicle (see red arrows in Figure 3). In this case the destination vehicle will receive the alerting message through the transmission link from the eNB/gNB, after the processing performed by the MEC or by the remote Cloud in the network. In Figure 3

Frame alignment [3, 5] [3, 5] [0.5, 1] [0.5, 1.5] [0.5, 1.5] PHY transmission [5, 8] [1, 2] [0.3, 0.8] [1, 2] [1, 2] eNB/gNB -MEC/GW [2, 5] [2, 5] [4, 6] - - GW-Cloud [3, 5] [3, 5] [4, 6] - -
In order to assess the latencies for each scenario and in each environment, in Table 2 we report the range of values, elaborated from [START_REF] Holma | 5G technology: 3GPP new radio[END_REF] and [START_REF] Giuliano | Communication network architectures for driver assistance systems[END_REF]. In our analysis, in addition to V2I and V2V, for V2N we considered (i ) two sub-carrier spacing (SCS) values of 15 kHz and 60 kHz, with a slot duration of 1 ms and 0.125 ms, respectively, and (ii ) a mini-slot case with 7 OFDM symbols. Moreover, we considered two types of scheduling i.e., (i ) Semi-Persistent Scheduling (SPS) and (ii ) Dynamic scheduling [START_REF] Garcia | A tutorial on 5G NR V2X communications[END_REF]. In the first case, we assume that the Physical Resource Block (PRB) is already scheduled and no contention has to be performed by the transmitting OBU. In the second case, before transmitting, the OBU has to send a scheduling request message, then waits for the PRB assignment through the Downlink Control Information (DCI) and finally, transmits the alert message [START_REF] Garcia | A tutorial on 5G NR V2X communications[END_REF]. Based on data collected in Table 2, in Figure 4 we show the multiple contributions for the computation of the latency Cumulative Distribution Function (CDF), computed for each communication mode (i.e., V2V, V2I, and V2N), in case of dynamic and semi-persistent scheduling, 4

However, in a real environment, packet loss can occur, thus increasing the 5 delay in communications between connected cars approaching to the crossroad.

6

It depends on the number of retransmissions and on the outage probability.

7

Then, for each communication mode i.e., V2V, V2I and V2N, and for each 8 environment i.e., UMi, UMa, and rural, the number of retransmissions varies 9 based on the distance between (i ) two cars, (ii ) the approaching car and the 10 RSU, and (iii ) the car and the eNB/gNB, respectively, as well as the interference 11 power. We can define the overall latency L [ms] as:

L m = N tx,avg (P out ) • δ m (CDF ≥ χ), (3)
where δ m [ms] is the delay occurring for a single alert message successful trans-13 mission, which depends on the m-th transmission mode i.e., m = [1, 2, 3], corre-14 sponding to V2V, V2I, and V2N, respectively, and is associated to the latency 15 CDF, which should be higher than a given threshold χ. For design purpose, it 16 is possible to consider the average or any other percentile value of the latency CDF , which is depicted in Figure 4 (e.g., the 90-th percentile or 99-th percentile). In Eq. ( 3), N tx,avg is the average number of transmissions necessary to deliver an alert message to a destination node, based on the outage probability P out obtained for a given scenario, which is equal to:

N tx,avg = 1 M M i=1 N tx,i (p i < P out ), (4)
where N tx,i is the number of transmissions necessary to deliver the i-th alert message, depending on a given transmission probability p i that should be lower than the outage probability associated to a given scenario and communication mode, and M is the number of messages to be delivered. Finally, MuSLi will select the optimal transmission mode (i.e., m ) defined as:

m = arg min L m . (5)

MuSLi technique

The main tasks of MuSLi approach are detailed in Algorithm This is mathematically expressed as:

O =        1 if N d n=1 O n < N d /2 + 1 0 otherwise (6)
Based on the given reference environment, the function Alert message f orwarding()

will
foreach n = [1, N d ] do foreach p ( ) c , = [1, v] do compute P c
Eq. ( 7) Obstacle is not detected and will be discarded the success of message delivery. In case of an error occurs in the r-th transmission of a warning message, with r = [1, . . . , N max,tx ], the vehicle that detected the crossing pedestrian will try again to send a message until a maximum number of transmission i.e., N max,tx . When the maximum number of attempts is reached i.e., r → N max,tx , the vehicle stops sending warning messages and no info is forwarded to the approaching vehicle, then achieving an infinity latency.

P
In case of obstacle detection but the obstacle is not present (i.e., false positive), the MuSLi algorithm still sends the warning messages. Similarly, if the obstacle detection is negative but the obstacle is present (i.e., false negative),

MuSLi does not send the warning messages. In order to limit these flaws, we introduced the possibility to consider multiple outputs of the neural network (i.e.,

O n ) in sequence from the neural network and take a decision on the pedestrian detection on a majority basis.

In order to improve the detection of the on board-mounted LiDAR, we assume that the vehicles close to the crossroad collect the LiDAR traces of the same scene autonomously, but from different points of view. Then, they share each own LiDAR trace with the other vehicles. Each vehicle is able to superimpose its own LiDAR trace with the other received traces, by properly translating and rotating them to synchronize them with its collected trace. Now, the neural network is able to provide whether (and where) a pedestrian is crossing the street. The superimposed traces increase the accuracy of the LiDAR detection, with respect to a single trace. An example of a single and a superimposed LiDAR point clouds is reported in Figure 5.

Multi-LiDAR Sensor Detection

Testbed for Evaluation

In order to evaluate the combination of multiple points of clouds from LiDAR sensors mounted on vehicles, we used the open-source simulator CARLA [START_REF] Dosovitskiy | CARLA: An open urban driving simulator[END_REF] for autonomous driving to implement the dedicated testbed. The flexibility and degree of realism offered by this simulator allowed creating complex environments, (a) (b) In order to enforce the reproducibility of our experiments, we configured the underlying rendering engine to perform in synchronous mode. Using this method, the rendering engine processes a single step and then it waits for the client permission to go ahead with another. We chose a single step duration equal to 50 ms of simulated time, thus obtaining 20 steps for 1 second. In this experiment, we started by spawning a pedestrian willing to cross the road In [START_REF] Wang | An efficient lidar point cloud map coding scheme based on segmentation and frame-inserting network[END_REF] some methods to remove the redundancy in time and space are proposed and analyzed.

Point clouds merging

The first vehicle of the environment is the ego car (i.e., vehicle 1). For this experiment, all the point clouds have to be references using the ego coordinate system. During the simulation, after obtaining each LiDAR scan, we translate and rotate the entire point cloud in respect to the ego car location. This is expressed as p

P c = v =1 p ( ) c , (7)
where v represents the number of vehicles involved into the LiDAR obstacle detection. It is worth noting that, more importantly, for increasing values of v, the obstacle detection will be more accurate.

At the end of this stage, we collected a total of four point clouds i.e., from v 1 to v 4 , for each snapshot. In the next section we will compare the pedestrian detection performances based on these configurations.

Pedestrian detection and evaluation

In 3D Object Detection, the aim is to predict three dimensional rotated bounding boxes from a point cloud. The output obtained from the point cloud merge state, is thus used as an input to a neural network in charge to detect the four pedestrians. In particular we compared the detection performances obtained using a single point cloud i.e., data from the ego car, with the merged point clouds i.e., combined LiDARs as from Eq. ( 7). The neural network pipeline is based on CenterPoint [START_REF] Yin | Center-based 3d object detection and tracking[END_REF], which is a two stages 3D Object Detection network. CenterPoint relies on the same keypoint estimation approach used in CenterNet [START_REF] Zhou | Objects as points[END_REF], which consists in predicting a heatmap Ŷ ∈ [0, 1] W ×H×K where W and H are respectively the width and height of the input image and K is the number of classes. Every local maximum of the predicted heatmap corresponds to an object center. For each of them, CenterNet regresses to a 2D bounding box by means of the size map Ŝ ∈ R W ×H×2 . In almost all neural networks for 3D

Object Detection, the point cloud is initially quantized into regular bins, which can be Voxels or Pillars, by the VoxelNet [START_REF] Zhou | Voxelnet: End-to-end learning for point cloud based 3d object detection[END_REF] or PointPillars [START_REF] Lang | Pointpillars: Fast encoders for object detection from point clouds[END_REF] backbones, respectively.

After this stage, an encoder is in charge of extracting features, producing a feature map M ∈ R W ×L×F , where W and L are width and length, and F is the number of channels. These dimensions depend on the backbone properties such as size or stride. In the same way as CenterNet, the feature map is used to obtain a heatmap where the peaks correspond to the object centers from a 2D bird-eye view. In the last stage of the neural network architecture, all the other 3D properties (i.e., boxes size and orientation) are regressed starting from the object centers. In particular, this stage is made of a shared twolayer MLP (i.e., multilayer perception), which is in charge of (i ) obtaining the class confidence score and (ii ) box regression prediction. For every detection, the network outputs a confidence score D ∈ [0, 1], which is compared with a threshold value D th . To complete the evaluation, we took advantage of an existing CenterPoint model pre-trained on the NuScenes dataset [START_REF] Caesar | nuscenes: A multimodal dataset for autonomous driving[END_REF], which is made of ≈ 390k LiDAR sweeps distributed in 1, 000 scenes. The NuScenes car equipment consists of 6 cameras, 1 LiDAR, 5 RADAR, GNSS and IMU. The LiDAR sensor is a Velodyne HDL32E, which has analogue specifications of our simulated one. This similarity allowed skipping the training part and assess the performances directly by inference on the simulated LiDAR sweeps.

At this point, we compared the detector output from the ego car versus the merged point clouds, with the ground truth snapshots made in the simulation environment. The evaluation metric for overlaps is 2D BEV (i.e., bird-eye view)

Intersection over Union (or IoU ), which is defined as:

IoU = A O A U , (8)
where A O and A U are the area of overlap and union, respectively. Although it is possible to evaluate the system using a 3D IoU [START_REF] Xu | 3d-giou: 3d generalized intersection over union for object detection in point cloud[END_REF], for the purpose of this work we considered adequate a simpler method, such as the classic one presented in the previous paragraph. Once an obstacle has been detected, the proposed system sends the warning message to the other cars approaching the crossroad, and provides them the pedestrian position detected by the LiDAR.

Nevertheless, concerning the accuracy necessary to provide the considered service (i.e., pedestrian detection and warning transmission), it is required that the approaching car is aware of the lane in which the pedestrian is crossing. In this way, this car can properly adapt its speed based on its planned route. In the event that the vehicle receives the crossing pedestrian's position that is in the lane is going to occupy, it should reduce its speed accordingly in order not to run over the pedestrian, otherwise it can take its motion unchanged.

In order to assess the performances of object detection, we relied on the commonly used metrics Precision P, Recall R and F 1 Score. These metrics are derived by first classifying the detector outcome in four distinct categories, which are: T p (true positives), F p (false positives), T n (true negatives) and F n (false negatives). Specifically, true positives are defined as the number of correctly identified bounding boxes. False positives are the detections that correspond to none of the ground truth boxes. False negatives are defined as the count of boxes, which were not detected. Eventually, true negatives are generally ignored in object detection tasks, and more significant in object classification. In our case, this value is always assumed equal to zero. The mathematical formulations of the considered metrics are expressed as follows:

P = T p T p + F p , (9)
and Precision and recall can be combined to define another metric, which is the F 1 Score, defined as the harmonic mean between P and R. It yields:

R = T p T p + F n . (10)
F 1 = 2 • P • R P + R , (11)
whose highest value is 1, which indicates perfect precision and recall.

Simulation results

In this section, the performance assessment of MuSLi will take into account (i ) the detection accuracy, and (ii ) network KPIs, such as the outage probability and latency related to message forwarding. In the latter case, each of these KPIs has been evaluated for different scenarios and transmission modes.

Regarding the pedestrian detection assessment, we investigated the on-board neural network behavior versus the confidence score threshold for the object detection i.e., D th . As shown by Figure 7, the threshold set to 0.4 maximizes the F 1 score. This value has been considered in the following simulation results. We initially evaluate the LiDAR clouds computed in case of (i ) traditional single vehicle detection, and (ii ) MuSLi approach, with one single and multiple LiDAR sensors mounted on different vehicles, respectively. In Figure 8, a comparison between the single vehicle (i.e., v = 1) and multiple vehicles (i.e., 2 ≤ v ≤ 5) performances is shown. It is worth noting that for this specific scenario, the performance gain obtained by adding the fourth vehicle is limited, and so we can approximate the most appropriate number of vehicles achieving high performance with MuSLi as 4. Indeed, adding the fifth car in the obstacle detection mechanism does not increase the overall performance gain, and the F 1 score for v = 5 is almost the same as the curve for v = 4. This result is due to the position of the fifth car, which is laying behind the first car and then its vision is "hindered" by this car. Notice that the choice of v = 4 is also due to the need of keeping low the computational cost, as it is linearly dependent on the number of vehicles involved in the object detection. Moreover, the presence of two more cars at the intersection allows the pedestrian detector to reach an F 1 score of 0.72 at the distance of 10 meters. Compared to F 1 = 0.29 obtained from a single vehicle at the same distance, we achieve a performance boost of 0.43, which corresponds to a 148% increase. Instead, if we consider the case which involves only the first two cars, the performance boost is equal to 0.17 that is a 58% increase, which is still enough to consider this kind of approach. Furthermore, from Figure 9 it is evident the uncertainty U defined as The position accuracy of the LiDAR is quite high (on the order of 20 cm at maximum). In our settings we considered the typical IoU value of 0.5 [START_REF] Yin | Center-based 3d object detection and tracking[END_REF].

U = 1 -F 1 is
Thus, assuming an area of a pedestrian of 50×30 cm 2 (in 2D from high view), the maximum error in localizing a pedestrian is 16.5 cm for F 1 values in Figure 8.

Notice that if some phenomena like blind spots occur, we still can obtain a similar accuracy by entirely removing the car from the network elements in charge of the obstacle detection, therefore assuming a total occlusion from that car. It follows that, for partially occluded cars, the F 1 score at a specific distance will fall between the curve computed for v cars i.e. F 1 (v), and that for (v -1) cars i.e., F 1 (v -1), where v is the number of the cars participating in the obstacle detection mechanism.

Network performance related to MuSLi technique is described as follows and are based on parameters in Table 1. Figure 10 depicts the outage probability in case of Urban Micro scenario [START_REF][END_REF] and for LoS and NLoS transmission modes.

Also, we assume two different cases for (i ) low interference (i.e., I /η + 1 = 5 dB) and (ii ) high interference (i.e., I /η + 1 = 10 dB), which both refer to noise raise with respect to the thermal noise power, respectively depicted in Figure 10 As expected, we observe that for a given distance, the outage probability increases. For instance, in case of low interference, the outage probability achieved with LoS and V2I communication mode is 10 -3 at ≈ 170 m, while the same value is reached at shorter distance i.e., ≈ 120 m, in case of high interference. Similar considerations can be taken observing the outage probabilities in case of Urban Macro and Rural scenarios, respectively in Figure 11 and Figure 12.

Moving from Urban Micro to Rural, the reachable transmission ranges with outage probability ≤ 10 -3 increase. For instance, in Figure 12 (a) for V2V

LoS and Rural scenario at low interference, the outage probability ≤ 10 -3 is guaranteed till 270 m, while the same value of outage probability is guaranteed till 190 m in case of Urban Macro, for V2I LoS and low interference, as depicted in Figure 11 (a). Finally, notice that in same cases, the outage probability for V2N transmission mode is not shown, due to favorable propagation conditions that occur mainly in Rural scenario (see Figure 12). In the following, we are now describing the performance of message forward-1 ing through MuSLi, in case that an obstacle has been detected. Without lack 2 of generality, we consider only the Urban Micro environment, and the main 3 parameters adopted in our simulations are reported in Table 2. In all the fig- 1.25 for P out = 0.2), while for P out = 0.9 (unrealistic case) the average number of retransmissions is 10. In the following simulations, we set the maximum number of retransmissions to 10.

In Figure 13 we have computed the message transmission latency for each transmission mode. We observe that latencies are slightly constant for low dis-

Conclusions

Due to the increasing popularity of connected vehicles, it is becoming essential to report the presence of obstacles on the roadway automatically by using on board advanced sensors such as infrared, electro-optical, acoustic, LiDAR, sonar, radar, etc. In this paper we presented the MuSLi technique, aiming to detect obstacles in a vehicular environment. Vehicles, defined as content islands, subscribe to an extended sensor service when approaching to an area of interest, and publish the contents collected by LiDAR sensors in order to improve the detection accuracy. In case of pedestrian detection, a source vehicle sends alert messages to other approaching vehicles through C-V2X. MuSLi is evaluated in case of specific propagation environments, transmission modes (i.e., LoS and NLoS, the type of implemented scheduling and the amount of interference power in the considering area.

Results are expressed in terms of outage transmissions and latency to deliver the alert messages. For example, they showed that V2V should be preferred up to 115 m in an Urban Micro environment in case a SPS strategy is adopted, while switching to V2I up to 160 m and to V2N (with SCS of 60 kHz) for greater distances of the approaching vehicle to the considered crossroad. In case of dynamic scheduling V2V should be preferred up to 85 m, switching to V2N (with SCS of 60 kHz) for greater distances.

As future work, we can consider LiDAR sensors mounted on fixed positions along the road, such as on RSUs placed on the traffic lights. In this case RSUs can "observe" the road from a position without obstacles i.e., in LoS. However, a few limitations can occur since the RSUs deployment is not always possible, such as in rural area where the vehicular density is low and pedestrian cannot be forced to cross the road to pedestrian crossings. On the other side, RSUs equipped with LiDAR sensors are useful in scenarios with low visibility due to road bends or bumps. Then, the deployment of LiDAR-based infrastructure nodes should be accordingly designed. Furthermore, the use of unmanned aerial vehicles can be investigated to improve the warning message transmission performance by properly locating them in specific areas.

Figure 1 :

 1 Figure 1: Schematic of content island vehicle, relying on MQTT for message exchange.

Figure 2 :

 2 Figure 2: Connected content islands reference scenario, where different communication and transmission modes are allowed.

  18 dB the losses due to the distance d [m] between the transmitter and the receiver, and η [W] is the thermal noise power. The ratio I /η is the noise raise caused by the extra interference due to other simultaneous communications occurring in the same Physical Resource Blocks (PRBs).

Figure 3 :

 3 Figure 3: Vehicular communication modes in the considered scenario.

  communication paths of the three communication modes are highlighted in black, blue and red lines, for V2V, V2I and V2N, respectively.

Figure 4 :

 4 Figure 4: Comparison of the latency CDF in case (a) semi-persistent and (b) dynamic scheduling, respectively.

  1. MuSLi is a technique for (i ) fast and accurate obstacle detection and (ii ) resulting alert message forwarding. For the obstacle detection, multiple cars are involved in this task, so that multiple LiDAR point clouds are collected and processed in order to compute a joint detection. If the detection score is higher than a given threshold, MuSLi will transmit alert messages related to the presence of an obstacle in a given position on the road. Messages transmission occurs through C-V2X communications, in case of V2V, V2I and V2N modes, and different transmissions links, such as LoS and NLoS.Let us assume that in a given reference environment i.e., Urban Micro, Urban Macro, and Rural, a source vehicle i.e., V T x , is approaching to an area of interest where it is likely to find an obstacle (e.g., a pedestrian crossing the street). It is able to communicate with its neighboring vehicles i.e., (v-1) vehicles, according to the m-th (with m = [1, 2, 3]) communication modes (i.e., V2V, V2I, and V2N, corresponding to m = 1, m = 2, and m = 3, respectively). The source vehicle and the other (v -1) vehicles are equipped with LiDAR sensors and periodically scan the environment acquiring clouds of LiDAR points. All these acquired data are exchanged among vehicles and the cumulated LiDAR cloud resulting from the sum of collected clouds by the other vehicles is the input for an on board neural network, detailed in Section 4, with the aim of pedestrian detection. As a result, MuSLi will provide information about the presence of finding an obstacle at distance d [m] from the source vehicle (or in general from a d cross distance from the area of interest).Let n = [1, 2, . . . , N d ] be the index of the output from the neural network, related to the obstacle detection decision. We assume that the n-th Boolean variable O n represents the single information about obstacle detected (i.e., O n = 1) or not (i.e., O n = 0) in case the n-th output of the neural network (i.e., the confidence score D n ) is higher than a given threshold D th (i.e., D ≥ D th ). In case that the majority of the N d outputs is positive, MuSLi will consider the obstacle as present and detected i.e., O = 1, and will act for message forwarding from the source vehicle to other approaching vehicles (i.e., N v ). On the other side, the obstacle is assumed as not present and will be discarded i.e., O = 0.

15 Algorithm 1 :r

 151 compute the outage probability P out towards the i-th recipient vehicle and the expected latency for the m-th communication modes (i.e., L m ). An optimization of the latency will occur, so that the source vehicle will be forced to transmit an alert message through the m-th communication mode that provides the lowest latency i.e., m . More details about the mathematical modeling of the MuSLi technique are provided in next Section 4. The proposed algorithm works under the constraint of a maximum allowable number of retransmissions of warning messages i.e., N max,tx . This parameter has been introduced in order to limit the latency in case of high values of outage probability that can affect MuSLi Pseudocode Input : Index of retransmissions N max,tx Maximum number of retransmissions Output: O Boolean variable for obstacle detection

  c enters the detector compute D Detection score if D ≥ D th then O n = 1 Single output of detected obstacle else O n = 0 Single output of obstacle not detected if O = N d n=1 O n ≥ N d /2 + 1 then Input : S Reference environment m Index of transmission mode N v Number of vehicles for forwarding D Set of V T x -N v distances Output: m Optimal transmission mode Alert message f orwarding(S, m) foreach i = [1, N v ] do compute P out (d i ) with d i ∈ D Outage probability compute L m Latency for V2V, V2I and V2N min m=[1,3] L m Optimal transmission mode selection m : L m = min m=[1,3] L m Compute m while r < N max,tx , r = [1, N max,tx ] do send message to the i-th vehicle by m if successful transmission then exit r + 1 ← r else

Figure 5 :

 5 Figure 5: Example of detection image obtained from (a) one single point cloud of a LiDAR sensor mounted on board of one vehicle, and (b) four superimposed clouds coming from multiple LiDAR sensors mounted on four different vehicles. The pedestrian detection is highlighted in the red circle.

Figure 6 :

 6 Figure 6: Simulated scenario for obstacle detection, where one pedestrian is crossing the area along the four pedestrian crossings.

  ., the point cloud from vehicle 1. Similarly, this applies to the other available vehicles. The transformed point clouds coming from the -th available vehicle i.e., p ( ) c , are then merged in a round-robin fashion with the previous ones, resulting into P c defined as follows:

Figure 7 :

 7 Figure 7: F 1 score versus the detection threshold.

Figure 8 :

 8 Figure 8: Comparison of the performance results (F 1 score) obtained for a single scan (i.e., v = 1) and multiple merged LiDAR scans (i.e., v ≥ 2), from one and multiple cars, respectively.

Figure 9 :

 9 Figure 9: Uncertaintly vs. the number of cars and for different distances to the crossroad.

  reduced as the number of cars sharing point clouds increases, for example from U = 0.28 to U = 0.1 at d cross = 0 m or from U = 0.7 to U = 0.25 at d cross = 10 m, corresponding to v = 1 and v = 5, respectively.

Figure 10 :

 10 Figure 10: Comparison of the outage probability versus the distance, in case Urban Micro scenario, for (a) low and (b) high interference, respectively.

  (a) and (b). Such values represent average lower and upper bounds and have been chosen to take into account the variability of interference due for example to the increase of the number of simultaneous transmitters in the considered area, thus investigating the outage probability ranges.

Figure 11 :

 11 Figure 11: Comparison of the outage probability versus the distance, in case of Urban Macro scenario, for (a) low and (b) high interference, respectively.

Figure 12 :

 12 Figure 12: Comparison of the outage probability versus the distance, in case of Rural scenario, for (a) low and (b) high interference, respectively.

Figure 13 :

 13 Figure 13: Semi-persistent scheduling. Comparison of the latency vs. the distance from a destination vehicle, for (a) V2V, and (b) V2I connectivity mode.

Figure 14 :Figure 15 : 1 CDFs, as depicted in Figure 4 .Figure 16 :Figure 17 :

 1415141617 Figure 14: Semi-persistent scheduling. Comparison of the latency vs. the distance from a destination vehicle, in case of V2N connectivity mode via Cloud, for (a) 15kHz, (b) 60kHz, and (c) minislot transmission.

  tances, while they increase as retransmissions are required when the outage in case of 60 kHz and minislot, which guarantees a latency between 20 ms and 80 ms in the range from d = 110 m and d = 180 m, overcoming V2I in the same range. Similar to the previous organization, from Figure 16 to Figure 18 latencies are for dynamic scheduling case. Latency for V2V in Figure 16 (a), for V2I in Figure 16 (b), for V2N-Cloud in Figure 17 (a) 15 kHz, (b) 60 kHz, and (c) minislot transmission) and for V2N-MEC in Figure 18 (a) 15 kHz, (b) 60 kHz, and (c) minislot transmission) are reported, respectively. Considering also in this case the example of NLoS and high interference values, V2V presents lower latencies (lower than 20 ms) but for reduced distancies (i.e., d ≤ 85 m). For d > 85 m, V2N-MEC at 60 kHz provides lower latencies for the alert message delivery. In case of mini-slot, V2N results comparable to V2V up to 50 m and overcomes it for greater distances.

Table 2 :

 2 Latency ranges [ms] in case of (i) Dynamic and (ii) Semi-persistent scheduling.

	Parameter	LTE/5G	5G	5G	V2I	V2V
		@15kHz @60kHz Mini-slot
		Dynamic scheduling
	Frame alignment	[3, 6]	[3, 6]	[0.5, 1]	[0.5, 1.5] [0.5, 1.5]
	PHY transmission	[15, 18]	[4, 5]	[0.3, 0.8]	[9, 11]	[9, 11]
	eNB/gNB -MEC/GW	[2, 5]	[2, 5]	[4, 6]	-	-
	GW-Cloud	[3, 5]	[3, 5]	[4, 6]	-	-
	Semi-Persistent Scheduling (SPS)

probability is not anymore negligible. The slope (i.e., smooth or fast) and the initial value (i.e., smaller or higher) of latency are both affected by the communication modes, the noise due to interferents, and the scheduling techniques.

Specifically, the semi-persistent scheduling provides lower latency than the case of dynamic scheduling due to absence of scheduling request procedure.

From Figure 13 to Figure 15 latencies are for semi-persistent scheduling case.

In Figure 13 (a) and (b) latency for V2V and V2I are reported, respectively.

In Figure 14