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

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

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.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Schreier, Matthias and Willert, Volker and Adamy, Jürgen
Title: Bayesian, Maneuver-Based, Long-Term Trajectory Prediction and Criticality Assessment for Driver Assistance Systems
Language: English
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.

Title of Book: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik
18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics
Event Title: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Event Location: Qingdao, China
Date Deposited: 20 Oct 2014 08:18
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