# Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

arXiv: https://arxiv.org/abs/2005.11064 · HTML: https://mlsanity.com/paper/2005.11064/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 30 of 31)

- [DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving](https://mlsanity.com/paper/2401.03641/) (2024)
- [MTD-GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersections](https://mlsanity.com/paper/2307.16118/) (2023)
- [TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving](https://mlsanity.com/paper/2203.16792/) (2022)
- [JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes](https://mlsanity.com/paper/2207.07895/) (2022)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Space](https://mlsanity.com/paper/2402.15790/) (2024)
- [A Cooperation-Aware Lane Change Method for Autonomous Vehicles](https://mlsanity.com/paper/2201.10746/) (2022)
- [Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems](https://mlsanity.com/paper/2406.10015/) (2024)
- [A Novel Driver Distraction Behavior Detection Method Based on Self-supervised Learning with Masked Image Modeling](https://mlsanity.com/paper/2306.00543/) (2023)
- [Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality](https://mlsanity.com/paper/2507.03002/) (2025)
- [A Safety-Critical Decision Making and Control Framework Combining Machine Learning and Rule-based Algorithms](https://mlsanity.com/paper/2201.12819/) (2022)
- [Interaction-Aware Decision-Making for Autonomous Vehicles in Forced Merging Scenario Leveraging Social Psychology Factors](https://mlsanity.com/paper/2309.14497/) (2023)
- [A Survey on Hybrid Motion Planning Methods for Automated Driving Systems](https://mlsanity.com/paper/2406.05575/) (2024)
- [Synergizing Decision Making and Trajectory Planning Using Two-Stage Optimization for Autonomous Vehicles](https://mlsanity.com/paper/2411.18974/) (2024)
- [Driving Like Yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.18757/) (2026)
- [WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.08375/) (2026)
- [Measuring Sociality in Driving Interaction](https://mlsanity.com/paper/2306.13992/) (2023)
- [Human-Like Implicit Intention Expression for Autonomous Driving Motion Planning: A Method Based on Learning Human Intention Priors](https://mlsanity.com/paper/2307.15950/) (2023)
- [Hierarchical Motion Planning and Offline Robust Model Predictive Control for Autonomous Vehicles](https://mlsanity.com/paper/2402.04769/) (2024)
- [Towards A General-Purpose Motion Planning for Autonomous Vehicles Using Fluid Dynamics](https://mlsanity.com/paper/2406.05708/) (2024)
- [Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections](https://mlsanity.com/paper/2409.17162/) (2024)
- [Towards Developing Socially Compliant Automated Vehicles: Advances, Expert Insights, and A Conceptual Framework](https://mlsanity.com/paper/2501.06089/) (2025)
- [Heterogeneous Decision Making in Mixed Traffic: Uncertainty-aware Planning and Bounded Rationality](https://mlsanity.com/paper/2502.18529/) (2025)
- [CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous Driving](https://mlsanity.com/paper/2504.02450/) (2025)
- [An Evolutionary Game-Theoretic Merging Decision-Making Considering Social Acceptance for Autonomous Driving](https://mlsanity.com/paper/2508.07080/) (2025)
- [Self-Interest and Systemic Benefits: Emergence of Collective Rationality in Mixed Autonomy Traffic Through Deep Reinforcement Learning](https://mlsanity.com/paper/2511.04883/) (2025)
- [Personalized Autonomous Driving via Optimal Control with Clearance Constraints from Questionnaires](https://mlsanity.com/paper/2602.08326/) (2026)
- [CarPLAN: Context-Adaptive and Robust Planning with Dynamic Scene Awareness for Autonomous Driving](https://mlsanity.com/paper/2603.12607/) (2026)
- [Agentic MPC for Semantic Control System Resynthesis](https://mlsanity.com/paper/2606.12774/) (2026)

## Abstract (arXiv)

Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on roads in the future for a long time, how to merge AVs into human drivers traffic ecology and minimize the effect of AVs and their misfit with human drivers, are issues worthy of consideration. Moreover, different passengers have different needs for AVs, thus, how to provide personalized choices for different passengers is another issue for AVs. Therefore, a human-like decision making framework is designed for AVs in this paper. Different driving styles and social interaction characteristics are formulated for AVs regarding driving safety, ride comfort and travel efficiency, which are considered in the modeling process of decision making. Then, Nash equilibrium and Stackelberg game theory are applied to the noncooperative decision making. In addition, potential field method and model predictive control (MPC) are combined to deal with the motion prediction and planning for AVs, which provides predicted motion information for the decision-making module. Finally, two typical testing scenarios of lane change, i.e., merging and overtaking, are carried out to evaluate the feasibility and effectiveness of the proposed decision-making framework considering different human-like behaviors. Testing results indicate that both the two game theoretic approaches can provide reasonable human-like decision making for AVs. Compared with the Nash equilibrium approach, under the normal driving style, the cost value of decision making using the Stackelberg game theoretic approach is reduced by over 20%.