# Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections

arXiv: https://arxiv.org/abs/2409.17162 · HTML: https://mlsanity.com/paper/2409.17162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Reward Design with Language Models](https://mlsanity.com/paper/2303.00001/) (2023)
- [Risk-sensitive Reinforcement Learning](https://mlsanity.com/paper/1311.2097/) (2013)
- [ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind](https://mlsanity.com/paper/2111.09189/) (2021)
- [Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach](https://mlsanity.com/paper/2005.11064/) (2020)
- [Theory of Mind as Intrinsic Motivation for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.01158/) (2023)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Theory of Mind for Deep Reinforcement Learning in Hanabi](https://mlsanity.com/paper/2101.09328/) (2021)
- [Cooperative Driving at Unsignalized Intersections Using Tree Search](https://mlsanity.com/paper/1902.01024/) (2019)
- [Game-theoretic Modeling of Traffic in Unsignalized Intersection Network for Autonomous Vehicle Control Verification and Validation](https://mlsanity.com/paper/1910.07141/) (2019)
- [Decision Making for Autonomous Vehicles at Unsignalized Intersection in Presence of Malicious Vehicles](https://mlsanity.com/paper/1904.10158/) (2019)
- [Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary](https://mlsanity.com/paper/2406.17807/) (2024)
- [Cooperation Method of Connected and Automated Vehicles at Unsignalized Intersections: Lane Changing and Arrival Scheduling](https://mlsanity.com/paper/2109.14175/) (2021)

## Abstract (arXiv)

In this paper, we propose a Q-learning based decision-making framework to improve the safety and efficiency of Autonomous Vehicles when they encounter other maliciously behaving vehicles while passing through unsignalized intersections. In Autonomous Vehicles, conventional reward signals are set as regular rewards regarding feedback factors such as safety and efficiency. In this paper, safety gains are modulated by variable weighting parameters to ensure that safety can be emphasized more in emergency situations. The framework proposed in this paper introduces first-order theory of mind inferences on top of conventional rewards, using first-order beliefs as additional reward signals. The decision framework enables Autonomous Vehicles to make informed decisions when encountering vehicles with potentially malicious behaviors at unsignalized intersections, thereby improving the overall safety and efficiency of Autonomous Vehicle transportation systems. In order to verify the performance of the decision framework, this paper uses Prescan/Simulink co-simulations for simulation, and the results show that the performance of the decision framework can meet the set requirements.