Control Meets Learning Seminar
Deployment of autonomous vehicles (AV) on public roads promises increases in efficiency and safety, and requires intelligent situation awareness. We wish to have autonomous vehicles that can learn to behave in safe and predictable ways, and are capable of evaluating risk, understanding the intent of human drivers, and adapting to different road situations. This talk describes an approach to learning and integrating risk and behavior analysis in the control of autonomous vehicles. I will introduce Social Value Orientation (SVO), which captures how an agent's social preferences and cooperation affect interactions with other agents by quantifying the degree of selfishness or altruism. SVO can be integrated in control and decision making for AVs. I will provide recent examples of self-driving vehicles capable of adaptation.