Biased-Attention Guided Risk Prediction for Safe Decision-Making at Unsignalized Intersections
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningAutonomous DrivingSimilar Papers 제목 키워드 기반
RAP: Risk-Aware Prediction for Robust Planning
Robust planning in interactive scenarios requires predicting the uncertain future to make risk-aware decisions. Unfortunately, due to long-tail safety-critical events, the risk is often under-estimated by finite-sampling…
PredictionKelp: A Streaming Safeguard for Large Models via Latent Dynamics-Guided Risk Detection
Large models (LMs) are powerful content generators, yet their open-ended nature can also introduce potential risks, such as generating harmful or biased content. Existing guardrails mostly perform post-hoc detection that…
Prediction of Drug-Induced TdP Risks Using Machine Learning and Rabbit Ventricular Wedge Assay
The evaluation of drug-induced Torsades de pointes (TdP) risks is crucial in drug safety assessment. In this study, we discuss machine learning approaches in the prediction of drug-induced TdP risks using preclinical dat…
BIG-bench Machine LearningPredictionRSafe: Incentivizing proactive reasoning to build robust and adaptive LLM safeguards
Large Language Models (LLMs) continue to exhibit vulnerabilities despite deliberate safety alignment efforts, posing significant risks to users and society. To safeguard against the risk of policy-violating content, syst…
Safety AlignmentRiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework
Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are …
Graph Neural NetworkManagement