Search Results for author:
Found 5 papers, 3 papers with code
SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially, in the presence of many aggressive, high-speed traffic participants.
Robotics Multiagent Systems
GAMMA: A General Agent Motion Model for Autonomous Driving
Further, the computational efficiency and the flexibility of GAMMA enable (i) simulation of mixed urban traffic at many locations worldwide and (ii) planning for autonomous driving in dense traffic with uncertain driver behaviors, both in real-time.
LeTS-Drive: Driving in a Crowd by Learning from Tree Search
LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.
PORCA: Modeling and Planning for Autonomous Driving among Many Pedestrians
Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time.
Robotics
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost.
