Foresee the Unseen

Sequential Reasoning about Hidden Obstacles for Safe Driving

Conference paper (2022)

Authors

Jose Manuel Gaspar Sanchez KTH Royal Institute of Technology
Truls Nyberg KTH Royal Institute of Technology, Scania CV AB
Christian Pek KTH Royal Institute of Technology
Jana Tumova KTH Royal Institute of Technology
Martin Torngren KTH Royal Institute of Technology
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Published Date

2022

Language

English

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Abstract

Safe driving requires autonomous vehicles to anticipate potential hidden traffic participants and other unseen objects, such as a cyclist hidden behind a large vehicle, or an object on the road hidden behind a building. Existing methods are usually unable to consider all possible shapes and orientations of such obstacles. They also typically do not reason about observations of hidden obstacles over time, leading to conservative anticipations. We overcome these limitations by (1) modeling possible hidden obstacles as a set of states of a point mass model and (2) sequential reasoning based on reachability analysis and previous observations. Based on (1), our method is safer, since we anticipate obstacles of arbitrary unknown shapes and orientations. In addition, (2) increases the available drivable space when planning trajectories for autonomous vehicles. In our experiments, we demonstrate that our method, at no expense of safety, gives rise to significant reductions in time to traverse various intersection scenarios from the CommonRoad Benchmark Suite.