DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Heejin | ko |
dc.contributor.author | Berntorp, Karl | ko |
dc.contributor.author | Inani, Pranav | ko |
dc.contributor.author | Ram, Arjun Jagdish | ko |
dc.contributor.author | Di Cairano, Stefano | ko |
dc.date.accessioned | 2022-07-06T03:00:13Z | - |
dc.date.available | 2022-07-06T03:00:13Z | - |
dc.date.created | 2022-07-06 | - |
dc.date.issued | 2021-09 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, v.29, no.5, pp.1907 - 1921 | - |
dc.identifier.issn | 1063-6536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/297260 | - |
dc.description.abstract | We describe the design and validation of a decision-making system in the guidance and control architecture for automated driving. The decision-making system determines the timing of transitions through a sequence of driving modes, such as lane following and stopping, for the vehicle to eventually arrive at the destination without colliding with obstacles, hence achieving safety and liveness. The decision-making system commands a transition to the next mode only when it is possible for an underlying motion planner to generate a feasible trajectory that reaches the target region of such next mode. Using forward and backward reachable sets based on a simplified dynamical model, the decision-making system determines the existence of a trajectory that reaches the target region, without actually computing it. Thus, the decision-making system achieves fast computation, resulting in reactivity to a varying environment and reduced computational burden. To handle the discrepancy between the dynamical model and the actual vehicle motion, we model it as a bounded disturbance set and guarantee robustness against it. We prove the safety and liveness of the decision-making system and validate it using small-scale car-like robots. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Reachability-Based Decision-Making for Autonomous Driving: Theory and Experiments | - |
dc.type | Article | - |
dc.identifier.wosid | 000682140300006 | - |
dc.identifier.scopusid | 2-s2.0-85112776451 | - |
dc.type.rims | ART | - |
dc.citation.volume | 29 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 1907 | - |
dc.citation.endingpage | 1921 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | - |
dc.identifier.doi | 10.1109/TCST.2020.3022721 | - |
dc.contributor.localauthor | Ahn, Heejin | - |
dc.contributor.nonIdAuthor | Berntorp, Karl | - |
dc.contributor.nonIdAuthor | Inani, Pranav | - |
dc.contributor.nonIdAuthor | Ram, Arjun Jagdish | - |
dc.contributor.nonIdAuthor | Di Cairano, Stefano | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Decision making | - |
dc.subject.keywordAuthor | Trajectory | - |
dc.subject.keywordAuthor | Planning | - |
dc.subject.keywordAuthor | Navigation | - |
dc.subject.keywordAuthor | Safety | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Heuristic algorithms | - |
dc.subject.keywordAuthor | Autonomous driving | - |
dc.subject.keywordAuthor | decision-making | - |
dc.subject.keywordAuthor | formal methods | - |
dc.subject.keywordAuthor | motion planning | - |
dc.subject.keywordPlus | ROAD VEHICLES | - |
dc.subject.keywordPlus | VERIFICATION | - |
dc.subject.keywordPlus | PREDICTION | - |
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