Pedestrian Collision Avoidance System for Scenarios with Occlusions
Safe autonomous driving in urban areas requires robust algorithms to avoid collisions with other traffic participants with limited perception ability. Current deployed approaches relying on Autonomous Emergency Braking (AEB) systems are often overly conservative. In this work, we formulate the problem as a partially observable Markov decision process (POMDP), to derive a policy robust to uncertainty in the pedestrian location. We investigate how to integrate such a policy with an AEB system that operates only when a collision is unavoidable. In addition, we propose a rigorous evaluation methodology on a set of well defined scenarios. We show that combining the two approaches provides a robust autonomous braking system that reduces unnecessary braking caused by using the AEB system on its own.
Code (1)
Tasks
Autonomous DrivingCollision AvoidanceSimilar Papers 제목 키워드 기반
Simulation of collision avoidance behavior in crowd movement by data-driven approach
Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory prediction accuracy under Euclidean metrics, yet they suffer from excessivel…
Trajectory PredictionCollision AvoidanceAssessing Localization Technologies for Pedestrian Collision Avoidance
Robust pedestrian safety is crucial to the next-generation of intelligent transportation systems. Such systems rely on active pedestrian localization and predictive collision alerts. Pedestrian localization can be suppor…
Collision AvoidanceCollision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning
Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians. This work proposes using deep reinforcement (RL) learning as a framework to model the complex interactions and cooper…
Collision AvoidanceDecision MakingDeep Reinforcement Learningreinforcement-learning+2Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where decisions of one agent directly affect the ot…
Autonomous VehiclesCollision AvoidanceDecision MakingDeep Reinforcement Learning+4Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network
Pedestrian detection and tracking in crowded video sequences have a wide range of applications, including autonomous driving, robot navigation and pedestrian flow surveillance. However, detecting and tracking pedestrians…
Autonomous DrivingCollision AvoidanceHead DetectionOptical Flow Estimation+2