The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Vehicle trajectory prediction based on motion model and maneuver recognition
Open accessAbstract
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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Citations
Cites
Lane change intent prediction for driver assistance: On-road design and evaluation
Morris, B., Doshi, A., & Trivedi, M. (2011) · 2011 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2011.5940538 · Cited by 228 · cited 1×
“In [10] the ego vehicle trajectory is predicted by defining a driving context which is a vector containing relevant signals from sensors of the testbed such as light indicator or driver’s gaze direction.”
Background
Cited by
Convolutional Social Pooling for Vehicle Trajectory Prediction
Deo, N., & Trivedi, M. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00196 · Cited by 1,050
Qayyum, A., Usama, M., Qadir, J., & Al-Fuqaha, A. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2975048 · Cited by 257
Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications
(2022) · doi:10.1109/tvt.2022.3170982 · Cited by 31
STFL:Spatio-temporal Federated Learning for Vehicle Trajectory Prediction
Zhou, X., Ke, R., Cui, Z., Liu, Q., & Qian, W. (2022) · 2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi55838.2022.9998967 · Cited by 20
Crowd Density Forecasting by Modeling Patch-Based Dynamics
Minoura, H., Yonetani, R., Nishimura, M., & Ushiku, Y. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3043169 · Cited by 16
Metadata
- Authors
- Houenou, A., Bonnifait, P., Cherfaoui, V., & Yao, W.
- Venue
- 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
- Publisher
- IEEE
- Published
- 2013-11
- Pages
- 4363-4369
- OpenAlex
- W2055556996
- Cited by
- 405
- References
- 1
- FWCI
- 11.07
- Type
- conference paper
- Language
- en
- Open access
- green
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- Houenou, A., Bonnifait, P., Cherfaoui, V., & Yao, W. (2013). Vehicle trajectory prediction based on motion model and maneuver recognition. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. https://doi.org/10.1109/iros.2013.6696982
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