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Communication Dans Un Congrès Année : 2013

Vehicle Trajectory Prediction based on Motion Model and Maneuver Recognition

Résumé

Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the ego-vehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on Constant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy.

Domaines

Automatique
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Dates et versions

hal-00881100 , version 1 (12-11-2013)

Identifiants

  • HAL Id : hal-00881100 , version 1

Citer

Adam Houenou, Philippe Bonnifait, Véronique Cherfaoui, Yao Wen. Vehicle Trajectory Prediction based on Motion Model and Maneuver Recognition. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), Nov 2013, Tokyo, Japan. pp.4363-4369. ⟨hal-00881100⟩
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