With cooperative intelligent transportation systems, vulnerable road users (VRU) safety can be enhanced by multiple means. On the one hand, perception systems are based on embedded sensors to protect VRUs. However, such systems may fail due to the sensors' visibility conditions and imprecision. On the other hand, vehicle-to-pedestrian (V2P) communication can contribute to the VRU safety by allowing vehicles and pedestrians to exchange information. This solution is, however, largely affected by the reliability of the exchanged information, which most generally is the GPS data. Since perception and communication have complementary features, we can expect that a fusion between these two approaches can be a solution to the VRU safety. In this paper, we propose a cooperative system that combines the outputs of communication and perception. After introducing theoretical models of both individual approaches, we develop a probabilistic association between perception and V2P communication information by means of multi-hypothesis tracking. Experimental studies are conducted to demonstrate the applicability of this approach in real-world environments. Our results show that the cooperative VRU protection system can benefit of the redundancy coming from the perception and communication technologies both in line-of-sight (LOS) and non-LOS conditions. We establish that the performances of this system are influenced by the classification performances of the perception system and by the accuracy of the GPS positioning transmitted by the communication system.

Urban environments are among the most challenging scenarios for car-following systems, since pedestrians may interfere with the platoon unexpectedly. To address this problem, this paper proposes a cooperative system using vehicle-to-vehicle and vehicle-to-pedestrian communication links. A fractional-order control-based cooperative adaptive cruise control benefits of communication for tighter inter-vehicle distances, while pedestrian communication is fused with LiDAR sensing to allow the detection of occluded pedestrians. The prediction of the pedestrians' trajectories is used to perform a speed reduction or an emergency braking that interrupts the car-following yif necessary. Whenever a platoon decoupling occurs, a gap-closing maneuver is executed so that the ego-vehicle rejoins the platoon in a string stable way. The complete system was tested on experimental platforms at inria facilities, providing encouraging results and demonstrating the correct performance of the integrated systems.

Collective perception messages (CPMs) inform neighbors about detected objects and their current status. Although CPMs can greatly contribute to improving vehicle perception, their performance will degrade if the wireless channel is saturated and/or vehicles must process excessively high amount of data to build such a perception. Channel saturation and processing overhead can be induced by transmissions of CPMs if vehicles and roadside infrastructure transmit CPMs too often with too much redundant information. Indeed, according to the current draft standard, the CPM generation frequency at individual vehicles and infrastructure can be as high as 10 Hz and depending on the perception capability, individual CPMs can be quite heavy (around 1500 Bytes), containing redundant information. It is hence important to integrate techniques that reduce information redundancy while providing sufficient perception with optimal resource use. In this paper, we propose context-aware communication schemes that control CPM content selection and transmission. Two types of contexts, particularly radio resource use (channel busy ratio: CBR) and infrastructure availability, have been considered in the proposed schemes. Using a network simulator, we evaluated the performances of the proposed schemes in terms of CBR, packet delivery ratio and awareness ratio. The simulation results show that the scheme that takes into account both resource use and infrastructure availability offers the best performance on all the above metrics.

Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments.

Neural receivers are emerging as key enablers for intelligent physical-layer design in next-generation wireless communications. However, the integration of learning-based architectures into practical communication systems requires careful consideration of standard compatibility and compu tational constraints. In this work, we propose a Hybrid Neural Receiver (H-NR) that operates on the frequency-domain OFDM resource grid and replaces the post-FFT baseband receiver chain, including resource grid demapping, channel estimation, equalization, demodulation, and channel decoding, within a unified end-to-end architecture. The proposed design integrates Transformer encoder blocks for global symbol inference with a Graph Neural Network (GNN) for structure-aware channel decoding. Extensive evaluations under varying vehicular speeds (0–120 km/h), carrier frequencies (5.9 and 28 GHz), and standardized UMa and CDL channel models demonstrate consistent gains of approximately 0.5 dB over the state-of-the-art TransRx neural receiver, particularly in scenarios where conventional receivers exhibit significant performance degradation. The proposed H-NR demonstrates efficient GPU utilization, achieving a throughput of 33.95 GFLOPs/s and a 121.7% improvement over the state-of-the-art TransRx. This performance, together with its ability to process diverse data modalities such as data, image, and audio, highlights its suitability for next-generation C-V2X systems

Les systèmes de transports intelligents coopératifs (C-ITS) offrent des opportunités pour améliorer la sécurité routière et particulièrement la sécurité des usagers vulnérables (VRU), e.g., piétons et cyclistes. La principale source d'accidents provient de l'incapacité des usagers, véhicules et VRUs, à détecter le danger avant qu'une collision soit inévitable. Nous introduisons un système de perception qui s'appuie sur les données des capteurs laser et caméra pour estimer l'état des VRUs entourant le véhicule. Une technique de classification multi-classes des obstacles routiers à partir de données laser a été développée en utilisant une méthode d'apprentissage statistique et une estimation bayésienne. Nous proposons une architecture de communication véhicules-piétons (V2P) qui prend en compte les faibles ressources énergétiques des smartphones transportés par les piétons. Notre solution s'appuie sur les standards définis dans l'architecture de communication véhiculaire ETSI ITS et propose une dissémination géographique pour la communication V2P. Un système coopératif perception/communication a le potentiel de gérer des scénarios de plus en plus complexes en combinant la capacité de la perception à estimer l'état dynamique des obstacles détectés et la capacité de la communication à échanger un contenu riche entre des usagers éloignés. Nous introduisons une fusion multi-hypothèses entre les informations de perception et de communication et une application pour smartphone destinée à protéger les VRUs des dangers de la route. Les solutions proposées au cours de la thèse sont évaluées sur des données réelles. Nous avons mené des expérimentations sur le campus d'INRIA démontrant les atouts d'un système coopératif de protection des usagers vulnérables.