Lane-Level Route Planning for Autonomous Vehicles
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We present an algorithm that, given a representation of a road network in lane-level detail, computes a route that minimizes the expected cost to reach a given destination. In doing so, our algorithm allows us to solve for the complex trade-offs encountered when trying to decide not just which roads to follow, but also when to change between the lanes making up these roads, in order to – for example – reduce the likelihood of missing a left exit while not unnecessarily driving in the leftmost lane. This routing problem can naturally be formulated as a Markov Decision Process (MDP), in which lane change actions have stochastic outcomes. However, MDPs are known to be time-consuming to solve in general. In this paper, we show that – under reasonable assumptions – we can use a Dijkstra-like approach to solve this stochastic problem, and benefit from its efficient O(n log n) running time. This enables an autonomous vehicle to exhibit natural lane-selection behavior as it efficiently plans an optimal route to its destination.
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