Search Results for author:
Found 2 papers, 0 papers with code
Deep Reinforcement Learning for Advanced Longitudinal Control and Collision Avoidance in High-Risk Driving Scenarios
Existing Advanced Driver Assistance Systems primarily focus on the vehicle directly ahead, often overlooking potential risks from following vehicles.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
