Motion planing in dynamic traffic scenes is a challenging problem. In particular, since it is unknown during planning whether a certain decision, such as passing another traffic participant on the left or right, will result in a safe and comfortable motion. Exhaustive exploration of all principle driving paths is computationally expensive, so that one typically reverts to heuristics — this, however can be unsatisfactory in situations when the heuristics fail to find a solution although it exists. We address this problem by computing the union of all possible motions for a sequence of high-level decisions (e.g. overtake vehicle on the left and then another one on the right), which we refer to as a driving corridor. Our proposed algorithm is over-approximative, i.e. the union of driving corridors provably encloses all possible motions. Thus, if the set of reachable positions within a driving corridor becomes empty, the corresponding sequence of high-level decisions is infeasible and can be discarded by the motion planner. Driving corridors also facilitate selecting high-level plans: Large driving corridors should be preferred since they provide more opportunities for optimizing motions and are more robust towards unpredicted changes. Numerical examples demonstrate the usefulness of our approach.
Computing possible driving corridors for automated vehicles
2017-06-01
946246 byte
Conference paper
Electronic Resource
English
Computing Possible Driving Corridors for Automated Vehicles
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