3 papers
cs.RO2021
Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments
Hung-Jui Huang, Kai-Chi Huang, Michal Čáp +3
Planning in environments with other agents whose future actions are uncertain often requires compromise between safety and performance. Here our goal is to design efficient plannin…
cs.RO2019
PODDP: Partially Observable Differential Dynamic Programming for Latent Belief Space Planning
Dicong Qiu, Yibiao Zhao, Chris L. Baker
Autonomous agents are limited in their ability to observe the world state. Partially observable Markov decision processes (POMDPs) formally model the problem of planning under worl…
cs.CV2019
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Tianyang Zhao, Yifei Xu, Mathew Monfort +5
Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, so…