According to the community database on accidents on the roads in Europe, 2035 cyclist fatalities happened in Europe in 2019 [S]. In Sweden, 10440 bicycle crashes were reported in the Swedish Traffic Accident Data Acquisition database during 2019, and 30% of the cyclist fatalities were in car-to-cyclist rear-end crashes [6]. Nowadays, new micromobility vehicles (MMVs), for example, e-scooters, and Segways, are becoming more popular. Unlike traditional bicycles, these new MMVs usually have novel designs in appearance, kinematics, operation method, and power source (e.g., electricity-driven/assisted), which bring new hazards to traditional road users [1, 4]. Thus, it is essential to understand and quantify the behavior of the new MMV users to improve road safety.

Cette thèse, réalisée en coopération avec l'Institut Pascal et Renault, s'inscrit dans le domaine des applications d'aide à la conduite, la plupart de ces systèmes visant à améliorer la sécurité des passagers du véhicule. La fusion de différents capteurs permet de rendre plus fiable la prise de décision. L'objectif des travaux de cette thèse a été de développer un système de fusion entre un radar et une caméra intelligente pour la détection des obstacles frontaux au véhicule. Nous avons proposé une architecture modulaire de fusion temps réel utilisant des données asynchrones provenant des capteurs sans a priori applicatif. Notre système de fusion de capteurs est basé sur des méthodes de suivi de plusieurs cibles. Des méthodes probabilistes de suivi de cibles ont été envisagées et une méthode particulière, basée sur la modélisation des obstacles par un ensemble fini de variables aléatoires a été choisie et testée en temps réel. Cette méthode, appelée CPHD (Cardinalized Probability Hypothesis Density) permet de gérer les différents défauts des capteurs (non détections, fausses alarmes, imprécision de positions et de vitesses mesurées) et les incertitudes liées à l’environnement (nombre inconnu d'obstacles à détecter). Ce système a été amélioré par la gestion de différents types d'obstacles : piéton, voiture, camion, vélo. Nous avons proposé aussi une méthode permettant de résoudre le problème des occultations avec une caméra de manière explicite par une méthode probabiliste en prenant en compte les imprécisions de ce capteur. L'utilisation de capteurs intelligents a introduit un problème de corrélation des mesures (dues à un prétraitement des données) que nous avons réussi à gérer grâce à une analyse de l'estimation des performances de détection de ces capteurs. Afin de compléter ce système de fusion, nous avons mis en place un outil permettant de déterminer rapidement les paramètres de fusion à utiliser pour les différents capteurs. Notre système a été testé en situation réelle lors de nombreuses expérimentations. Nous avons ainsi validé chacune des contributions de manière qualitative et quantitative. / This PhD work, carried out in collaboration with Institut Pascal and Renault, is in the field of the Advanced Driving Assisted Systems, most of these systems aiming to improve passenger security. Sensors fusion makes the system decision more reliable. The goal of this PhD work was to develop a fusion system between a radar and a smart camera, improving obstacles detection in front of the vehicle. Our approach proposes a real-time flexible fusion architecture system using asynchronous data from the sensors without any prior knowledge about the application. Our fusion system is based on a multi targets tracking method. Probabilistic multi target tracking was considered, and one based on random finite sets (modelling targets) was selected and tested in real-time computation. The filter, named CPHD (Cardinalized Probability Hypothesis Density), succeed in taking into account and correcting all sensor defaults (non detections, false alarms and imprecision on position and speed estimated by sensors) and uncertainty about the environment (unknown number of targets). This system was improved by introducing the management of the type of the target: pedestrian, car, truck and bicycle. A new system was proposed, solving explicitly camera occlusions issues by a probabilistic method taking into account this sensor imprecision. Smart sensors use induces data correlation (due to pre-processed data). This issue was solved by correcting the estimation of sensor detection performance. A new tool was set up to complete fusion system: it allows the estimation of all sensors parameters used by fusion filter. Our system was tested in real situations with several experimentations. Every contribution was qualitatively and quantitatively validated.

Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.