Risk-averse Behavior Planning for Autonomous Driving under Uncertainty
This addresses safety and decision-making challenges for autonomous driving systems, but appears incremental as it modifies existing methods rather than introducing a new paradigm.
The paper tackles the problem of autonomous vehicle behavior planning under uncertainty by proposing a scalable framework that integrates QMDP, unscented transform, and Monte Carlo tree search (MCTS), with a modification to the MCTS action selection to improve robustness.
Autonomous vehicles have to navigate the surrounding environment with partial observability of other objects sharing the road. Sources of uncertainty in autonomous vehicle measurements include sensor fusion errors, limited sensor range due to weather or object detection latency, occlusion, and hidden parameters such as other human driver intentions. Behavior planning must consider all sources of uncertainty in deciding future vehicle maneuvers. This paper presents a scalable framework for risk-averse behavior planning under uncertainty by incorporating QMDP, unscented transform, and Monte Carlo tree search (MCTS). It is shown that upper confidence bound (UCB) for expanding the tree results in noisy Q-value estimates by the MCTS and a degraded performance of QMDP. A modification to action selection procedure in MCTS is proposed to achieve robust performance.