# Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles

arXiv: https://arxiv.org/abs/1904.10158 · HTML: https://mlsanity.com/paper/1904.10158/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Modeling Human Driving Behavior through Generative Adversarial Imitation Learning](https://mlsanity.com/paper/2006.06412/) (2020)
- [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)
- [A taxonomy of strategic human interactions in traffic conflicts](https://mlsanity.com/paper/2109.13367/) (2021)
- [Revealed Multi-Objective Utility Aggregation in Human Driving](https://mlsanity.com/paper/2303.07435/) (2023)
- [Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections](https://mlsanity.com/paper/2409.17162/) (2024)

## Abstract (arXiv)

In this paper, we investigate the decision making of autonomous vehicles in an unsignalized intersection in presence of malicious vehicles, which are vehicles that do not respect the law by not using the proper rules of the right of way. Each vehicle computes its control input as a Nash equilibrium of a game determined by the priority order based on its own belief: each of non-malicious vehicle bases its order on the law, while a malicious one considers itself as having priority. To illustrate our method, we provide numerical simulations, with different scenarios given by different cases of malicious vehicles.