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

arXiv: https://arxiv.org/abs/2207.03673 · HTML: https://mlsanity.com/paper/2207.03673/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2022.

## Built on by (most cited first, 14 of 14)

- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [Residual Q-Learning: Offline and Online Policy Customization without Value](https://mlsanity.com/paper/2306.09526/) (2023)
- [RoadBEV: Road Surface Reconstruction in Bird's Eye View](https://mlsanity.com/paper/2404.06605/) (2024)
- [Residual-MPPI: Online Policy Customization for Continuous Control](https://mlsanity.com/paper/2407.00898/) (2024)
- [SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction](https://mlsanity.com/paper/2410.08669/) (2024)
- [Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework](https://mlsanity.com/paper/2302.06803/) (2023)
- [Learning Online Belief Prediction for Efficient POMDP Planning in Autonomous Driving](https://mlsanity.com/paper/2401.15315/) (2024)
- [Importance Sampling-Guided Meta-Training for Intelligent Agents in Highly Interactive Environments](https://mlsanity.com/paper/2407.15839/) (2024)
- [HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions](https://mlsanity.com/paper/2510.12733/) (2025)
- [Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach](https://mlsanity.com/paper/2409.13993/) (2024)
- [Hi-Drive: Hierarchical POMDP Planning for Safe Autonomous Driving in Diverse Urban Environments](https://mlsanity.com/paper/2409.18411/) (2024)
- [A Two-stage Based Social Preference Recognition in Multi-Agent Autonomous Driving System](https://mlsanity.com/paper/2310.03303/) (2023)
- [Optimal Driver Warning Generation in Dynamic Driving Environment](https://mlsanity.com/paper/2411.06306/) (2024)
- [Fair Play in the Fast Lane: Integrating Sportsmanship into Autonomous Racing Systems](https://mlsanity.com/paper/2503.03774/) (2025)

## Abstract (arXiv)

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.