Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving

TL;DR

This paper presents an efficient game-theoretic planning method for autonomous driving that integrates Monte Carlo Tree Search with a prediction heuristic, enabling socially-compliant and diverse driving behaviors in complex interactions.

Contribution

It introduces a novel MCTS-based trajectory planning algorithm with a prediction heuristic and Bayesian inference for social compliance and driver preference modeling.

Findings

Effective in highly interactive scenarios

Generates diverse socially-compliant behaviors

Reduces computational resources compared to existing methods

Abstract

Planning under social interactions with other agents is an essential problem for autonomous driving. As the actions of the autonomous vehicle in the interactions affect and are also affected by other agents, autonomous vehicles need to efficiently infer the reaction of the other agents. Most existing approaches formulate the problem as a generalized Nash equilibrium problem solved by optimization-based methods. However, they demand too much computational resource and easily fall into the local minimum due to the non-convexity. Monte Carlo Tree Search (MCTS) successfully tackles such issues in game-theoretic problems. However, as the interaction game tree grows exponentially, the general MCTS still requires a huge amount of iterations to reach the optima. In this paper, we introduce an efficient game-theoretic trajectory planning algorithm based on general MCTS by incorporating a prediction algorithm as a heuristic. On top of it, a social-compliant reward and a Bayesian inference algorithm are designed to generate diverse driving behaviors and identify the other driver's driving preference. Results demonstrate the effectiveness of the proposed framework with datasets containing naturalistic driving behavior in highly interactive scenarios.