详情

Design autonomous vehicle behaviors in heterogeneous traffic flow

项目名称：	Design autonomous vehicle behaviors in heterogeneous traffic flow
摘要：	The benefits of autonomous vehicles (AVs) not only depend on the maturity of technologies, but also how AVs behave and interact with their peers and human-driven vehicles (HVs). Similar to many other systems, individual and collective dynamics of traffic flow are not always aligned with each other (for instance, aggressive driving may benefit an individual driver but disrupts the overall traffic). It is therefore imperative to consider behavior design for AVs such that the benefits of AVs can be realized at both individual and collective levels, even absent of centralized control. Behavior protocols for AVs will define their “driving styles”, in terms of information perception, utility, and opportunisticity. One possibility is letting all AVs be “human-like”, as did in existing literature. This research will explore more sophisticated behavior designs based on system principles and data. The research team will explore two approaches. The first approach is game-theoretic. In this approach, the team starts from defining agent utilities and casts interactions of heterogeneous agents as a spatial game. When a potential function for this game can be constructed, the team may prove the existence of its equilibria, derive conditions that lead to the desirable equilibria, and design AV behavior protocols based on these characterizations. From models of similar nature (known as Schelling’s models, which reproduce residential segregation), the team anticipates that with proper behavior protocols AVs can spontaneously form into platoons, even without centralized controls. The second approach is data-driven, leveraging deep reinforcement learning and big traffic data. In this approach, the team will train AVs as reinforcement learning (RL) agents from real-world trajectory data. Behavioral protocols are then obtained as the RL agents are endowed with reward functions of different structures. The team will identify the reward structures that best balance the individual and system goals and quantify the corresponding effects through simulations.
状态：	Active
资金：	113973
资助组织：	Office of the Assistant Secretary for Research and Technology
项目负责人：	Kline, Robin
执行机构：	University of California, Davis
开始时间：	20210401
预计完成日期：	20220331
主题领域：	Highways；Operations and Traffic Management；Planning and Forecasting；Vehicles and Equipment