Lane-Level Route Planning for Autonomous Vehicles

• Published in: arXiv.org
• Main Authors: Jones, Mitchell, Haas-Heger, Maximilian, van den Berg, Jur
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 13.07.2023
• Subjects: Algorithms; Computer Science - Robotics; Lane changing; Markov processes; Roads; Route planning; Run time (computers)
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2206.02883

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 lane-selection behavior as it efficiently plans an optimal route to its destination.