# Hierarchical Game-Theoretic Planning for Autonomous Vehicles

arXiv: https://arxiv.org/abs/1810.05766 · HTML: https://mlsanity.com/paper/1810.05766/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2018.

## Built on by (most cited first, 30 of 48)

- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Modeling Human Driving Behavior through Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2006.06412/) (2020)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models](https://mlsanity.com/paper/2104.10558/) (2021)
- [Deep Structured Reactive Planning](https://mlsanity.com/paper/2101.06832/) (2021)
- [End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving](https://mlsanity.com/paper/2104.08862/) (2021)
- [Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach](https://mlsanity.com/paper/2302.00171/) (2023)
- [SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction](https://mlsanity.com/paper/2110.00843/) (2021)
- [It Takes Two: Learning to Plan for Human-Robot Cooperative Carrying](https://mlsanity.com/paper/2209.12890/) (2022)
- [Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach](https://mlsanity.com/paper/2202.07720/) (2022)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios](https://mlsanity.com/paper/2107.04538/) (2021)
- [Predictive Planner for Autonomous Driving with Consistency Models](https://mlsanity.com/paper/2502.08033/) (2025)
- [Learning Human Rewards by Inferring Their Latent Intelligence Levels in Multi-Agent Games: A Theory-of-Mind Approach with Application to Driving Data](https://mlsanity.com/paper/2103.04289/) (2021)
- [A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering](https://mlsanity.com/paper/2108.12820/) (2021)
- [SOCIALMAPF: Optimal and Efficient Multi-Agent Path Finding with Strategic Agents for Social Navigation](https://mlsanity.com/paper/2210.08390/) (2022)
- [Learning Preferences for Interactive Autonomy](https://mlsanity.com/paper/2210.10899/) (2022)
- [Blending Data-Driven Priors in Dynamic Games](https://mlsanity.com/paper/2402.14174/) (2024)
- [Fully Unified Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2504.12667/) (2025)
- [Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework](https://mlsanity.com/paper/2302.06803/) (2023)
- [I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames](https://mlsanity.com/paper/2109.09807/) (2021)
- [Generalized dynamic cognitive hierarchy models for strategic driving behavior](https://mlsanity.com/paper/2109.09861/) (2021)
- [Presenting Multiagent Challenges in Team Sports Analytics](https://mlsanity.com/paper/2303.13660/) (2023)
- [Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization](https://mlsanity.com/paper/2306.08815/) (2023)
- [Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach](https://mlsanity.com/paper/2409.13993/) (2024)
- [Adaptive Agent Architecture for Real-time Human-Agent Teaming](https://mlsanity.com/paper/2103.04439/) (2021)
- [Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards](https://mlsanity.com/paper/2206.00886/) (2022)
- [Decision-Making Among Bounded Rational Agents](https://mlsanity.com/paper/2210.08672/) (2022)
- [Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors](https://mlsanity.com/paper/2309.14497/) (2023)
- [Decision-Making for Autonomous Vehicles with Interaction-Aware Behavioral Prediction and Social-Attention Neural Network](https://mlsanity.com/paper/2310.20148/) (2023)

## Abstract (arXiv)

The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle's planning and its predictions of other drivers' behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology. Unfortunately, dynamic games are too computationally demanding to meet the real-time constraints of autonomous driving in its continuous state and action space. In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into a long-horizon "strategic" game with simplified dynamics and full information structure, and a short-horizon "tactical" game with full dynamics and a simplified information structure. The value of the strategic game is used to guide the tactical planning, implicitly extending the planning horizon, pushing the local trajectory optimization closer to global solutions, and, most importantly, quantitatively accounting for the autonomous vehicle and the human driver's ability and incentives to influence each other. In addition, our approach admits non-deterministic models of human decision-making, rather than relying on perfectly rational predictions. Our results showcase richer, safer, and more effective autonomous behavior in comparison to existing techniques.