Search Results for author:
Found 3 papers, 0 papers with code
Dynamically Conservative Self-Driving Planner for Long-Tail Cases
In this way, the DCP is designed to automatically adjust to be more conservative in low-confidence "long-tail" cases while keeping efficient otherwise.
Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving
By constraining the uncertainty, the DRL model's performance is always greater than that of a baseline policy.
Long-Tail Prediction Uncertainty Aware Trajectory Planning for Self-driving Vehicles
To this end, this work proposes a trajectory planner to consider the prediction model uncertainty arising from insufficient data for safer performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
