Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

The aim of this paper is to study the contribution of intervehicle communication (IVC) to reduce the number of secondary collisions caused by an accident. We assess the impact of broadcast protocols used to disseminate warning messages and the impact of IVC penetration ratio on the number of collisions. There are two main contributions in this paper. First, it addresses the analytical evaluation of broadcast protocols in Vehicular Ad hoc NETworks (VANETs). We propose a general framework based on point processes to evaluate the performance of this protocol. Our approach is based on the theory of point processes and Palm calculus. We derive, for a generic frame error rate (FER) function, simple formulas for computing the delay required to propagate messages. These formulas hold for any FER function, enabling the comparison of broadcast performance under different radio propagation environments. In the second part of this paper, we propose an analytical evaluation of the number of vehicles involved in a collision. This analysis allows us to study the impact of radio technology penetration ratio and the impact of safety message delivery delays on the number of collisions.

With the growing development of Cooperative, Connected, and Automated Mobility (CCAM), questions arise about the real impact of this innovative mobility on our daily life. CCAM originally promised to improve road safety. It is now a holistic solution for future mobility: the CCAM is there to optimize traffic, which can translate into strategies for reducing energy consumption or polluting emissions, without compromising road safety. The capability of CCAM is dependent on the reliability and robustness of its components, as it will be making life-impacting decisions. It is therefore necessary to be able to guarantee a high-level quality of sensors, communication, software, and hardware architecture. In this mobility ecosystem, the infrastructure and data that it will be able to produce is at the heart of current research issues. This paper addresses the following question: Are the Connected and automated Vehicles (CAVs) the silver bullet solution with which to answer the issues of the current mobility systems? This question is discussed by investigating the technologies used, the digital infrastructures, its robustness to cyber-attack, and their relationship with the claimed benefits on safety, energy and pollution management, traffic optimization, deployment strategy, and a link with the new generation of road infrastructures.

In road traffic, mental overload often leads to a failure to notice new and distinctive stimuli. Such phenomenon is known as 'inattentional blindness'. Safe and efficient interaction between automated vehicles (AVs) and pedestrians is expected to rely heavily on external human-machine interfaces (eHMIs), a tool AVs are equipped with to communicate their intentions to pedestrians. This study seeks to explore the phenomenon of 'inattentional blindness' in the context of pedestrian-AV interactions. Specifically, the aim is to understand the effects of a warning eHMI on pedestrians' crossing decisions when they are engaged in a secondary task. In an experiment study with videos of pedestrian crossing scenarios filmed from the perspective of the crossing pedestrian, participants had to decide the latest point at which they would be willing to cross the road in front of an AV with an eHMI vs. an AV without an eHMI. Participants were also asked to predict the future behavior of the AV. 125 female and 9 male participants aged between 18 and 25 completed the experiment and a follow-up questionnaire. It was found that the presence of a warning eHMI on AVs contributes to a clearer understanding of pedestrians' inferences about the intention of AVs and helps deter late and dangerous crossing decisions made by pedestrians. However, the eHMI fail to help pedestrians avoid such decisions when they face a high mental workload induced by secondary task engagement.

Truck platooning is a promising technology in freight transport. To commercialize truck platooning as early as possible, its evaluation is in urgent need. For truck platooning evaluation, simulation platforms play a crucial role. However, there has not been a simulation platform to meet the evaluation needs of various stakeholders, including Original Equipment Manufacturers (OEMs), Freight Operators (FOs) and Transportation Management Administrations (TMAs). To fill the research gap, this paper proposes a next-generation simulation platform. It integrates a traffic simulator, platoon management system, and truck control module to satisfy all the evaluation needs. The proposed platform bears the following features: i) Compatibility with various platooning decision makers, planners, controllers, vehicle types and platoon management strategies; ii) Capability of evaluating platoon performance on the lateral dimension; iii) Prototype platoon management system provided for FOs; iv) Capability of evaluating truck platoon management performance in terms of sustainability and economy; v) Capability of evaluating the impact of interactive background traffic on platoon performance. vi) Capability of evaluating the impact of truck platoon management on traffic mobility. The proposed platform is validated by comparison against an actual field test. Its credibility is confirmed in terms of truck platoon performance and interactive traffic simulation. Additional tests are conducted to evaluate truck platoon performance and the impact of truck platoons on mixed traffic. The results reveal that existing platoon lane-change technologies should be upgraded to be compatible with high-traffic-demand scenarios. It is also revealed that a localized and up-to-date assessment is required before allowing truck platooning.