# Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments

arXiv: https://arxiv.org/abs/2402.11467 · HTML: https://mlsanity.com/paper/2402.11467/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 18 Feb 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 13 of 13)

- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving](https://mlsanity.com/paper/2401.03160/) (2024)
- [Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning](https://mlsanity.com/paper/1809.02926/) (2018)
- [A Non-Cooperative Game Approach to Autonomous Racing](https://mlsanity.com/paper/1712.03913/) (2017)
- [Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction](https://mlsanity.com/paper/2004.03053/) (2020)
- [An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors](https://mlsanity.com/paper/2005.11059/) (2020)
- [Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook](https://mlsanity.com/paper/2107.01110/) (2021)
- [Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios](https://mlsanity.com/paper/1904.06025/) (2019)
- [Interactive Decision Making for Autonomous Vehicles in Dense Traffic](https://mlsanity.com/paper/1909.12914/) (2019)
- [Safe Deep Policy Adaptation](https://mlsanity.com/paper/2310.08602/) (2023)

## Built on by (most cited first, 1 of 1)

- [Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective](https://mlsanity.com/paper/2503.12637/) (2025)

## Abstract (arXiv)

This paper proposes an adaptive behavioral decision-making method for autonomous vehicles (AVs) focusing on complex merging scenarios. Leveraging principles from non-cooperative game theory, we develop a vehicle interaction behavior model that defines key traffic elements and integrates a multifactorial reward function. Maximum entropy inverse reinforcement learning (IRL) is employed for behavior model parameter optimization. Optimal matching parameters can be obtained using the interaction behavior feature vector and the behavior probabilities output by the vehicle interaction model. Further, a behavioral decision-making method adapted to dynamic environments is proposed. By establishing a mapping model between multiple environmental variables and model parameters, it enables parameters online learning and recognition, and achieves to output interactive behavior probabilities of AVs. Quantitative analysis employing naturalistic driving datasets (highD and exiD) and real-vehicle test data validates the model's high consistency with human decision-making. In 188 tested interaction scenarios, the average human-like similarity rate is 81.73%, with a notable 83.12% in the highD dataset. Furthermore, in 145 dynamic interactions, the method matches human decisions at 77.12%, with 6913 consistence instances. Moreover, in real-vehicle tests, a 72.73% similarity with 0% safety violations are obtained. Results demonstrate the effectiveness of our proposed method in enabling AVs to make informed adaptive behavior decisions in interactive environments.