Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
This work addresses safety challenges for autonomous vehicles in complex unsignalized intersection scenarios, representing an incremental improvement over existing methods.
The paper tackled the problem of autonomous driving decision-making at unsignalized intersections by proposing a deep reinforcement learning framework with a biased attention mechanism for risk prediction, resulting in improved traffic efficiency and vehicle safety in simulations.
Autonomous driving decision-making at unsignalized intersections is highly challenging due to complex dynamic interactions and high conflict risks. To achieve proactive safety control, this paper proposes a deep reinforcement learning (DRL) decision-making framework integrated with a biased attention mechanism. The framework is built upon the Soft Actor-Critic (SAC) algorithm. Its core innovation lies in the use of biased attention to construct a traffic risk predictor. This predictor assesses the long-term risk of collision for a vehicle entering the intersection and transforms this risk into a dense reward signal to guide the SAC agent in making safe and efficient driving decisions. Finally, the simulation results demonstrate that the proposed method effectively improves both traffic efficiency and vehicle safety at the intersection, thereby proving the effectiveness of the intelligent decision-making framework in complex scenarios. The code of our work is available at https://github.com/hank111525/SAC-RWB.