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Foresee the Unseen: Sequential Reasoning about Hidden Obstacles for Safe Driving
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.ORCID iD: 0000-0001-9982-578X.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-2069-6581
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0001-7461-920x
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for Autonomous Systems, CAS. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-4173-2593
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

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.

Keywords [en]
Autonomous Vehicles, Hidden Traffic Participants, Traffic Occlusions, Motion Planning, Reachability Analysis, Safe Autonomy
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-308467OAI: oai:DiVA.org:kth-308467DiVA, id: diva2:1635726
Note

Published in 2022 IEEE Intelligent Vehicles Symposium (IV), Aachen, 04-09 June 2022, see https://ieeexplore.ieee.org/document/9827171

QC 20220208

Available from: 2022-02-07 Created: 2022-02-07 Last updated: 2022-10-27Bibliographically approved

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Gaspar Sánchez, José ManuelNyberg, TrulsPek, ChristianTumova, JanaTörngren, Martin

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Gaspar Sánchez, José ManuelNyberg, TrulsPek, ChristianTumova, JanaTörngren, Martin
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MechatronicsRobotics, Perception and Learning, RPLCentre for Autonomous Systems, CASACCESS Linnaeus Centre
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