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A crucial challenge to efficient and robust motion planning for autonomousvehicles is understanding the intentions of the surrounding agents. Ignoringthe intentions of the other agents in dynamic environments can lead to risky orover-conservative plans. In this work, we model the motion planning problem asa partially observable Markov decision process (POMDP) and propose an onlinesystem that combines an intent recognition algorithm and a POMDP solver togenerate risk-bounded plans for the ego vehicle navigating with a number ofdynamic agent vehicles. The intent recognition algorithm predicts theprobabilistic hybrid motion states of each agent vehicle over a finite horizonusing Bayesian filtering and a library of pre-learned maneuver motion models.We update the POMDP model with the intent recognition results in real time andsolve it using a heuristic search algorithm which produces policies withupper-bound guarantees on the probability of near colliding with other dynamicagents. We demonstrate that our system is able to generate better motion plansin terms of efficiency and safety in a number of challenging environmentsincluding unprotected intersection left turns and lane changes as compared tothe baseline methods.

Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments