Risk-Aware Autonomous Driving with Linear Temporal Logic Specifications
Humans naturally balance the risks of different concerns while driving, including traffic rule violations, minor accidents, and fatalities. However, achieving the same behavior in autonomous systems remains an open problem. This paper extends a risk metric that has been verified in human-like driving studies to encompass more complex driving scenarios specified by linear temporal logic (LTL) that go beyond just collision risks. This extension incorporates the timing and severity of events into LTL specifications, thereby reflecting a human-like risk awareness. Without sacrificing expressivity for traffic rules, we adopt LTL specifications composed of safety and co-safety formulas, allowing the control synthesis problem to be reformulated as a reachability problem. By leveraging occupation measures, we formulate a linear programming (LP) problem for this LTL-based risk metric. Consequently, the synthesized policy balances different types of risks, including not only collision risks but also traffic rule violations. The effectiveness of the proposed approach is validated by three typical traffic scenarios in the Carla simulator.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingAutonomous VehiclesDecision MakingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
STRAP: Spatial-Temporal Risk-Attentive Vehicle Trajectory Prediction for Autonomous Driving
Accurate vehicle trajectory prediction is essential for ensuring safety and efficiency in fully autonomous driving systems. While existing methods primarily focus on modeling observed motion patterns and interactions wit…
Trajectory PredictionAutonomous DrivingRisk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
End-to-end paradigm has emerged as a promising approach to autonomous driving. However, existing single-agent end-to-end pipelines are often constrained by occlusion and limited perception range, resulting in hazardous d…
Trajectory PlanningAutonomous DrivingRisk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, t…
Autonomous DrivingMotion PlanningOpenAI Gymreinforcement-learning+1Learning A Unified Risk Map for Autonomous Driving in Partially Observable Environments
Occlusion-aware prediction remains a critical challenge in autonomous driving due to the inherent uncertainty of unobserved regions. Existing approaches either overestimate risk based on reachable states or struggle to p…
Autonomous DrivingBadDreamer: Transferable Backdoor Attacks against Video World Models for Autonomous Driving
Video world models are increasingly used in autonomous driving to forecast future scene evolution and provide future-aware spatio-temporal representations for downstream action prediction. In perception-to-action pipelin…
Autonomous Driving