Title

Collective rationality from self-interested agents in mixed autonomy traffic

Abstract

The rapid development of self-driving technologies makes it imperative to characterize and model the behaviors of mixed autonomy traffic. The problem is especially important, yet more challenging, when automated vehicles are self-interested, instead of being centrally controlled or coordinated. In this talk, I will present my recent research towards solving this problem. I will first introduce a game theory model of mixed autonomy traffic. An intriguing finding is that, under mild conditions, agents in mixed autonomy traffic can always attain Pareto-efficient Nash equilibria, also known as “collective rationality”, even if they are all self-interested and behave differently. Then I will present a data-driven numerical approach to model mixed traffic flow and show how the model achieves good accuracy through capturing the complicated inter-class interactions from real-world traffic data.

Presentation

Dr. Jai Li's Presentation

Speaker Bio

Dr. Jia Li is an Assistant Adjunct Professor in the Department of Civil and Environmental Engineering, University of California, Davis. He holds Ph.D. in Transportation from UC Davis. His primary research interest is the modeling, control, and behavior design of automated vehicles in complex mixed autonomy environments leveraging game theory and artificial intelligence tools. He also works on applying data science methods to solve emerging traffic safety and mobility problems. He has published more than twenty peer-reviewed papers and led several research projects sponsored by different national and state agencies.

Speaker Headshot

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