Asset Details
Do you wish to reserve the book?
Multi-Modal Vehicle Trajectory Prediction by Collaborative Learning of Lane Orientation, Vehicle Interaction, and Intention
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Multi-Modal Vehicle Trajectory Prediction by Collaborative Learning of Lane Orientation, Vehicle Interaction, and Intention
Do you wish to request the book?
Multi-Modal Vehicle Trajectory Prediction by Collaborative Learning of Lane Orientation, Vehicle Interaction, and Intention
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Multi-Modal Vehicle Trajectory Prediction by Collaborative Learning of Lane Orientation, Vehicle Interaction, and Intention
2022
Overview
Accurate trajectory prediction is an essential task in automated driving, which is achieved by sensing and analyzing the behavior of surrounding vehicles. Although plenty of research works have been invested in this field, it is still a challenging subject due to the environment’s complexity and the driving intention uncertainty. In this paper, we propose a joint learning architecture to incorporate the lane orientation, vehicle interaction, and driving intention in vehicle trajectory forecasting. This work employs a coordinate transform to encode the vehicle trajectory with lane orientation information, which is further incorporated into various interaction models to explore the mutual trajectory relations. Extracted features are applied in a dual-level stochastic choice learning to distinguish the trajectory modality at both the intention and motion levels. By collaborative learning of lane orientation, interaction, and intention, our approach can be applied to both highway and urban scenes. Experiments on the NGSIM, HighD, and Argoverse datasets demonstrate that the proposed method achieves a significant improvement in prediction accuracy compared with the baseline.
Publisher
MDPI AG,MDPI
