Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments. (arXiv:1904.02341v1 [cs.RO])

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

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