Widespread adoption of autonomous vehicles will not become a reality until solutions are developed that enable these intelligent agents to co-exist with humans. This includes safely and efficiently interacting with human-driven vehicles, especially in both conflictive and competitive scenarios. Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are still inefficient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A prerequisite for realizing this inter-agent coordination is creating an effective and reliable means of communication among agents that enables them to share their situational awareness and constitute a mass intelligence. Cellular Vehicle-to-everything (C-V2X) communication is introduced to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. I investigate the reliability of these vehicular networks and propose a framework that enables them to scale to large groups of vehicles. A group of autonomous and human-driven vehicles (HVs) that are equipped with such reliable vehicular communication can work together to optimize an altruistic social utility ---as opposed to the egoistic individual utility--- and co-exist seamlessly. Achieving this mission without explicit coordination among agents is challenging, mainly due to the difficulty of predicting the behavior of humans with heterogeneous preferences in mixed-autonomy environments. Formally, I model an AV's maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using a multi-agent reinforcement learning framework. I build up on the prior work on socially-aware navigation and borrow the concept of social value orientation from psychology ---that formalizes how much importance a person allocates to the welfare of others--- in order to induce altruistic behavior in autonomous driving. In contrast with existing works that explicitly model the behavior of human drivers and rely on their expected response to create opportunities for cooperation, the Sympathetic Cooperative Driving (SymCoDrive) paradigm trains altruistic agents that realize safe and smooth traffic flow in competitive driving scenarios only from experiential learning and without any explicit coordination. I demonstrate a significant improvement in both safety and traffic-level metrics as a result of this altruistic behavior and importantly conclude that the level of altruism in agents requires proper tuning as agents that are too altruistic also lead to sub-optimal traffic flow.
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Pourmohammadi Fallah, Yaser
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Electrical and Computer Engineering
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Toghi, Behrad, "Cooperative Driving in Mixed-Autonomy Environments" (2021). Electronic Theses and Dissertations, 2020-. 1350.
Restricted to the UCF community until June 2027; it will then be open access.