Social behavior for autonomous vehicles

The authors present a framework that integrates social psychology tools into controller design for autonomous vehicles. The authors' key insight utilizes Social Value Orientation (SVO), quantifying an agent’s degree of selfishness or altruism, which allows the authors to better predict driver behavior. The authors model interactions between human and autonomous agents with game theory and the principle of best response. The authors' unified algorithm estimates driver SVOs and incorporates their predicted trajectories into the autonomous vehicle’s control while respecting safety constraints. The authors study common-yet-difficult traffic scenarios: highway merging and unprotected left turns. Incorporating SVO reduces error in predictions by 25%, validated on 92 human driving merges. Furthermore, the authors find that merging drivers are more competitive than nonmerging drivers.Deployment of autonomous vehicles on public roads promises increased efficiency and safety. It requires understanding the intent of human drivers and adapting to their driving styles. Autonomous vehicles must also behave in safe and predictable ways without requiring explicit communication. The authors integrate tools from social psychology into autonomous-vehicle decision making to quantify and predict the social behavior of other drivers and to behave in a socially compliant way. A key component is Social Value Orientation (SVO), which quantifies the degree of an agent’s selfishness or altruism, allowing the authors to better predict how the agent will interact and cooperate with others. The authors model interactions between agents as a best-response game wherein each agent negotiates to maximize their own utility. The authors solve the dynamic game by finding the Nash equilibrium, yielding an online method of predicting multiagent interactions given their SVOs. This approach allows autonomous vehicles to observe human drivers, estimate their SVOs, and generate an autonomous control policy in real time. The authors demonstrate the capabilities and performance of the authors' algorithm in challenging traffic scenarios: merging lanes and unprotected left turns. The authors validate their results in simulation and on human driving data from the NGSIM dataset. The authors' results illustrate how the algorithm’s behavior adapts to social preferences of other drivers. By incorporating SVO, the authors improve autonomous performance and reduce errors in human trajectory predictions by 25%.


  • English

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  • Accession Number: 01725031
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 13 2019 9:30AM