# Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits

arXiv: https://arxiv.org/abs/2201.01428 · HTML: https://mlsanity.com/paper/2201.01428/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2022.

## Built on by (most cited first, 7 of 7)

- [Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.24152/) (2024)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach](https://mlsanity.com/paper/2409.13993/) (2024)
- [Generative Models and Connected and Automated Vehicles: A Survey in Exploring the Intersection of Transportation and AI](https://mlsanity.com/paper/2403.10559/) (2024)
- [SPformer: A Transformer Based DRL Decision Making Method for Connected Automated Vehicles](https://mlsanity.com/paper/2409.15105/) (2024)
- [Deep Fictitious Play-Based Potential Differential Games for Learning Human-Like Interaction at Unsignalized Intersections](https://mlsanity.com/paper/2506.12283/) (2025)
- [Large Language Model based Interactive Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2604.23513/) (2026)

## Abstract (arXiv)

To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users. Under the proposed decision-making framework, in this work, a representative urban driving scenario, i.e. the unsignalized intersection, is investigated. Once the vehicle enters the focused zone, it will interact with other CAVs and make collaborative decisions. To evaluate the safety risk of surrounding vehicles and reduce the complexity of the decision-making algorithm, the driving risk assessment algorithm is designed with a Gaussian potential field approach. The decision-making cost function is constructed by considering the driving safety and passing efficiency of CAVs. Additionally, decision-making constraints are designed and include safety, comfort, efficiency, control and stability. Based on the cost function and constraints, the fuzzy coalitional game approach is applied to the decision-making issue of CAVs at unsignalized intersections. Two types of fuzzy coalitions are constructed that reflect both individual and social benefits. The benefit allocation in the two types of fuzzy coalitions is associated with the driving aggressiveness of CAVs. Finally, the effectiveness and feasibility of the proposed decision-making framework are verified with three test cases.