Latest revision as of 21:17, 28 January 2021

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.

Original document

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

• https://hal.archives-ouvertes.fr/hal-00881100/file/IROS13_PIN_161867_.pdf

• http://xplorestaging.ieee.org/ielx7/6679723/6696319/06696982.pdf?arnumber=6696982,

http://dx.doi.org/10.1109/iros.2013.6696982

• https://hal.archives-ouvertes.fr/hal-00881100,

https://hal.archives-ouvertes.fr/hal-00881100/document,

https://hal.archives-ouvertes.fr/hal-00881100/file/IROS13_PIN_161867_.pdf

• https://ieeexplore.ieee.org/document/6696982,

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