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Bayesian, Maneuver-Based, Long-Term Trajectory Prediction and Criticality Assessment for Driver Assistance Systems

Schreier, Matthias ; Willert, Volker ; Adamy, Jürgen :
Bayesian, Maneuver-Based, Long-Term Trajectory Prediction and Criticality Assessment for Driver Assistance Systems.
In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)
[Konferenz- oder Workshop-Beitrag], (2014)

Kurzbeschreibung (Abstract)

We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about immi- nent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the im- manent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) – a generalization of the common Time-To-Collision (TTC) in ar- bitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2014
Autor(en): Schreier, Matthias ; Willert, Volker ; Adamy, Jürgen
Titel: Bayesian, Maneuver-Based, Long-Term Trajectory Prediction and Criticality Assessment for Driver Assistance Systems
Sprache: Englisch
Kurzbeschreibung (Abstract):

We propose a Bayesian trajectory prediction and criticality assessment system that allows to reason about immi- nent collisions of a vehicle several seconds in advance. We first infer a distribution of high-level, abstract driving maneuvers such as lane changes, turns, road followings, etc. of all vehicles within the driving scene by modeling the domain in a Bayesian network with both causal and diagnostic evidences. This is followed by maneuver-based, long-term trajectory predictions, which themselves contain random components due to the im- manent uncertainty of how drivers execute specific maneuvers. Taking all uncertain predictions of all maneuvers of every vehicle into account, the probability of the ego vehicle colliding at least once within a time span is evaluated via Monte-Carlo simulations and given as a function of the prediction horizon. This serves as the basis for calculating a novel criticality measure, the Time-To-Critical-Collision-Probability (TTCCP) – a generalization of the common Time-To-Collision (TTC) in ar- bitrary, uncertain, multi-object driving environments and valid for longer prediction horizons. The system is applicable from highly-structured to completely non-structured environments and additionally allows the prediction of vehicles not behaving according to a specific maneuver class.

Buchtitel: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Fachbereich(e)/-gebiet(e): Fachbereich Elektrotechnik und Informationstechnik
Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik
Fachbereich Elektrotechnik und Informationstechnik > Institut für Automatisierungstechnik > Regelungsmethoden und Robotik
Veranstaltungstitel: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Veranstaltungsort: Qingdao, China
Hinterlegungsdatum: 20 Okt 2014 08:18
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