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Found 5 papers, 1 papers with code
Using Collision Momentum in Deep Reinforcement Learning Based Adversarial Pedestrian Modeling
Recent research in pedestrian simulation often aims to develop realistic behaviors in various situations, but it is challenging for existing algorithms to generate behaviors that identify weaknesses in automated vehicles' performance in extreme and unlikely scenarios and edge cases.
Pedestrian Emergence Estimation and Occlusion-Aware Risk Assessment for Urban Autonomous Driving
Here, we propose a pedestrian emergence estimation and occlusion-aware risk assessment system for urban autonomous driving.
An online evolving framework for advancing reinforcement-learning based automated vehicle control
With the latest stochastic model and given criteria, the action-reviser module checks validity of the controller's chosen action by predicting future states.
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
Automated driving in urban settings is challenging.
An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks
Within the framework, the evolving Finite State Machine (e-FSM), which is an online model able to (1) determine states uniquely as needed, (2) recognize states, and (3) identify state-transitions, is introduced.
