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. / Cooperative intelligent transportation systems (C-ITS) have the opportunity to enhance road safety, especially the safety of vulnerable road users (VRU), e.g., pedestrians and cyclists. Road accidents are mainly due to vehicles' and VRUs' inability to detect the danger before a collision cannot be avoided.We introduce a perception system based on laser and camera sensors to estimate the state of VRUs located around the vehicle. A multi-class classification of road obstacles based on laser data has been developed using statistical machine learning and Bayesian estimation.We propose an architecture for vehicles-to-pedestrians (V2P) communication which considers the weak energy resources of the devices carried by pedestrians such as smartphones. Our solution is relying on the standards defined by ETSI ITS architecture for vehicular communication and proposes geographical dissemination for V2P communication.A cooperative perception/communication system can deal with scenarios which are becoming more and more complex by combining the ability of perception to estimate the dynamic state of detected obstacles and the ability of communication to exchange a rich content between distant users. We introduce a multi-hypotheses fusion between perception and communication information and a smartphone application dedicated to protect VRUs from road danger.The solutions proposed during this thesis are evaluated on real data. We carried out real experiments on INRIA campus demonstrating the assets of a cooperative system for the protection of vulnerable road users.

Future vehicles are expected to be equipped with wireless communication technology, that enables them to be “connected” to each others and road infrastructures. Complementing current autonomous vehicles and automated driving systems, the wireless communication allows the vehicles to interact, cooperate, and be aware of its surroundings beyond their own sensors’ range. Such sys- tems are often referred to as Cooperative Intelligent Transport Systems (C-ITS), which aims to provide extra safety, efficiency, and sustainability to transporta- tion systems. Several C-ITS applications are under development and will require thorough testing and evaluation before their deployment in the real-world. C- ITS depend on several sub-systems, which increase their complexity, and makes them difficult to evaluate. Simulations are often used to evaluate many different automotive applications, including C-ITS. Although they have been used extensively, simulation tools dedicated to determine all aspects of C-ITS are rare, especially human factors aspects, which are often ignored. The majority of the simulation tools for C-ITS rely heavily on different combinations of network and traffic simulators. The human factors issues have been covered in only a few C-ITS simulation tools, that involve a driving simulator. Therefore, in this thesis, a C-ITS simulation framework that combines driving, network, and traffic simulators is presented. The simulation framework is able to evaluate C-ITS applications from three perspectives; a) human driver; b) wireless communication; and c) traffic systems. Cooperative Adaptive Cruise Control (CACC) and its applications are chosen as the first set of C-ITS functions to be evaluated. Example scenarios from CACC and platoon merging applications are presented, and used as test cases for the simulation framework, as well as to elaborate potential usages of it. Moreover, approaches, results, and challenges from composing the simulation framework are presented and discussed. The results shows the usefulness of the proposed simulation framework.