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 POMDPbased 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.The authors are with the
Partially observable Markov decision processes (POMDPs) have been successfully applied to various robot motion planning tasks under uncertainty. However, most existing POMDP algorithms assume a discrete state space, while the natural state space of a robot is often continuous. This paper presents Monte Carlo Value Iteration (MCVI) for continuous-state POMDPs. MCVI samples both a robot's state space and the corresponding belief space, and avoids inefficient a priori discretization of the state space as a grid. Both theoretical results and preliminary experimental results indicate that MCVI is a promising new approach for robot motion planning under uncertainty.
The partially observable Markov decision process (POMDP) provides a principled mathematical model for integrating perception and planning, a major challenge in robotics. While there are efficient algorithms for moderately large discrete POMDPs, continuous models are often more natural for robotic tasks, and currently there are no practical algorithms that handle continuous POMDPs at an interesting scale. This paper presents an algorithm for continuous-state, continuousobservation POMDPs. We provide experimental results demonstrating its potential in robot planning and learning under uncertainty and a theoretical analysis of its performance. A direct benefit of the algorithm is to simplify model construction.
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