Abstract

International audience; 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.


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The different versions of the original document can be found in:

http://dx.doi.org/10.1109/iros.2013.6696982
https://hal.archives-ouvertes.fr/hal-00881100/document,
https://hal.archives-ouvertes.fr/hal-00881100/file/IROS13_PIN_161867_.pdf
https://hal.archives-ouvertes.fr/hal-00881100/document,
http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_IROS_2013/media/files/1647.pdf,
https://dblp.uni-trier.de/db/conf/iros/iros2013.html#HouenouBCY13,
https://hal.archives-ouvertes.fr/hal-00881100,
https://dx.doi.org/10.1109/IROS.2013.6696982,
http://ieeexplore.ieee.org/document/6696982,
http://dx.doi.org/10.1109/IROS.2013.6696982,
https://doi.org/10.1109/IROS.2013.6696982,
https://academic.microsoft.com/#/detail/2055556996
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Published on 01/01/2013

Volume 2013, 2013
DOI: 10.1109/iros.2013.6696982
Licence: CC BY-NC-SA license

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