Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections
This addresses safety for autonomous vehicles in specific traffic scenarios, but it is incremental as it builds on existing methods with minor enhancements.
The paper tackles the problem of autonomous vehicle safety and efficiency at unsignalized intersections when encountering malicious vehicles, proposing a Q-learning framework with modulated safety rewards and first-order theory of mind inferences, and simulation results show it meets set requirements.
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.