Vers des approches hybrides fondées sur l'émergence et l'apprentissage : prise en compte des véhicules autonomes dans le trafic

According to the World Health Organization, road accidents cause almost 1.2 million deaths and 40 million injuries each year. In wealthy countries, safety standards prevent a large proportion of accidents. The remaining accidents are caused by human behavior. For this reason, some are planning to automate road traffic, i.e., to replace humans as drivers of their vehicles. However, automating road traffic can hardly be achieved overnight. Thus, driving robots (DRs) and human drivers could cohabit in mixed traffic. Our thesis focuses on the safety issues that may arise due to behavioral differences between DRs and human drivers. DRs are designed to respect formal norms, those of the Highway Code. Human drivers, on the other hand, are opportunistic, not hesitating to break formal norms and adopt new, informal ones. The emergence of new behaviors in traffic can make it more heterogeneous and encourage accidents caused by misinterpretation of these new behaviors. We believe that minimizing this behavioral heterogeneity would reduce the above risks. Therefore, our thesis proposes a decision-making model of DR whose behavior is intended to be close to non-hazardous human practices, in order to minimize the heterogeneity between RC and human driver behavior, and with the aim of promoting their acceptance by the latter. To achieve this, we will adopt a multidisciplinary approach, inspired by studies in driving psychology and combining traffic simulation, multi-agent reinforcement learning (MARL). MARL consists of learning a behavior by trial and error guided by a utility function. Thanks to its ability to generalize, especially via neural networks, MARL can be adapted to any environment, including traffic. We will use it to teach our decision model robust behavior in the face of the diversity of traffic situations. To avoid incidents, DR manufacturers could design relatively homogeneous and defensive behaviors rather than opportunistic ones. However, this approach risks making DRs predictable and, therefore, vulnerable to opportunistic behavior by human drivers. The consequences could then be detrimental to both traffic fluidity and safety. Our first contribution aims at reproducing heterogeneous traffic, i.e., where each vehicle exhibits a unique behavior. We assume that by making the behavior of DRs heterogeneous, their predictability will be reduced and opportunistic human drivers will be less able to anticipate their actions. Therefore, this paradigm considers the behavioral heterogeneity of DRs as a critical feature for the safety and fluidity of mixed traffic. In an experimental phase, we will demonstrate the ability of our model to produce heterogeneous behavior while meeting some of the challenges of MARL. Our second contribution will be the integration of informal norms into the decision processes of our DR decision model. We will focus exclusively on integrating the notion of social orientation value, which describes individuals' social behaviors such as altruism or selfishness. Starting with a highway merging scenario, we will evaluate the impact of social orientation on the fluidity and safety of merging vehicles. We will show that altruism can improve safety, but that its actual impact is highly dependent on traffic density.