Intention-aware online POMDP planning for autonomous driving in a crowd

TLDR

To drive near pedestrians safely, efficiently, and smoothly, autonomous vehicles must estimate unknown pedestrian intentions and hedge against the uncertainty in intention estimates in order to choose actions that are effective and robust. This paper presents an intention‑aware online planning approach for autonomous driving amid many pedestrians. The method employs a POMDP for systematic, robust online planning that incorporates pedestrian intention estimates. Experiments demonstrate that the POMDP‑based planner operates near real time at 3 Hz on a robot golf cart in a complex, dynamic environment, indicating rapid improvements in computational efficiency and practical applicability for robot planning under uncertainty.

Abstract

This paper presents an intention-aware online planning approach for autonomous driving amid many pedestrians. To drive near pedestrians safely, efficiently, and smoothly, autonomous vehicles must estimate unknown pedestrian intentions and hedge against the uncertainty in intention estimates in order to choose actions that are effective and robust. A key feature of our approach is to use the partially observable Markov decision process (POMDP) for systematic, robust decision making under uncertainty. Although there are concerns about the potentially high computational complexity of POMDP planning, experiments show that our POMDP-based planner runs in near real time, at 3 Hz, on a robot golf cart in a complex, dynamic environment. This indicates that POMDP planning is improving fast in computational efficiency and becoming increasingly practical as a tool for robot planning under uncertainty.