Belief State Planning for Autonomously Navigating Urban Intersections
This addresses the challenge of safe and efficient autonomous driving in complex urban environments, but it is incremental as it builds on existing POMDP methods.
The paper tackled the problem of autonomous vehicles navigating unsignalized urban intersections by framing it as a POMDP and solving it with a Monte Carlo sampling method, resulting in a policy that outperformed a threshold-based heuristic in simulation on safety and efficiency metrics.
Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.