# Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach

arXiv: https://arxiv.org/abs/2305.03563 · HTML: https://mlsanity.com/paper/2305.03563/

## Key facts

- Cited by 8 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 5 of its 43 references that are in the map.
- First posted on arXiv 5 May 2023.

## Placed in

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

## Builds on (most cited first, 5 of 5)

- [Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning](https://mlsanity.com/paper/2010.03118/) (2020)
- [Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts](https://mlsanity.com/paper/1810.00829/) (2018)
- [Cooperative Driving at Unsignalized Intersections Using Tree Search](https://mlsanity.com/paper/1902.01024/) (2019)
- [Towards Provably Not-at-Fault Control of Autonomous Robots in Arbitrary Dynamic Environments](https://mlsanity.com/paper/1902.02851/) (2019)
- [A Game-Theoretic Analysis of the Social Impact of Connected and Automated Vehicles](https://mlsanity.com/paper/1909.10033/) (2019)

## Built on by (most cited first, 8 of 8)

- [Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework](https://mlsanity.com/paper/2409.12812/) (2024)
- [Automated Vehicles Should be Connected with Natural Language](https://mlsanity.com/paper/2507.01059/) (2025)
- [LeAD: The LLM Enhanced Planning System Converged with End-to-end Autonomous Driving](https://mlsanity.com/paper/2507.05754/) (2025)
- [Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow](https://mlsanity.com/paper/2506.12600/) (2025)
- [Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality](https://mlsanity.com/paper/2507.03002/) (2025)
- [Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation](https://mlsanity.com/paper/2509.01868/) (2025)
- [Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2509.25751/) (2025)
- [How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Concept](https://mlsanity.com/paper/2608.09144/) (2026)

## Abstract (arXiv)

High-density, unsignalized intersection has always been a bottleneck of efficiency and safety. The emergence of Connected Autonomous Vehicles (CAVs) results in a mixed traffic condition, further increasing the complexity of the transportation system. Against this background, this paper aims to study the intricate and heterogeneous interaction of vehicles and conflict resolution at the high-density, mixed, unsignalized intersection. Theoretical insights about the interaction between CAVs and Human-driven Vehicles (HVs) and the cooperation of CAVs are synthesized, based on which a novel cooperative decision-making framework in heterogeneous mixed traffic is proposed. Normalized Cooperative game is concatenated with Level-k game (NCL game) to generate a system optimal solution. Then Lattice planner generates the optimal and collision-free trajectories for CAVs. To reproduce HVs in mixed traffic, interactions from naturalistic human driving data are extracted as prior knowledge. Non-cooperative game and Inverse Reinforcement Learning (IRL) are integrated to mimic the decision making of heterogeneous HVs. Finally, three cases are conducted to verify the performance of the proposed algorithm, including the comparative analysis with different methods, the case study under different Rates of Penetration (ROP) and the interaction analysis with heterogeneous HVs. It is found that the proposed cooperative decision-making framework is beneficial to the driving conflict resolution and the traffic efficiency improvement of the mixed unsignalized intersection. Besides, due to the consideration of driving heterogeneity, better human-machine interaction and cooperation can be realized in this paper.