Vision-based Intention and Trajectory Prediction in Autonomous Vehicles: A Survey
Izzeddin Teeti, Salman Khan, Ajmal Shahbaz, Andrew Bradley, Fabio Cuzzolin
Abstract
This survey targets intention and trajectory prediction in Autonomous Vehicles (AV), as AV companies compete to create dedicated prediction pipelines to avoid collisions. The survey starts with a formal definition of the prediction problem and highlights its challenges, to then critically compare the models proposed in the last 2-3 years in terms of how they overcome these challenges. Further, it lists the latest methodological and technical trends in the field and comments on the efficacy of different machine learning blocks in modelling various aspects of the prediction problem. It also summarises the popular datasets and metrics used to evaluate prediction models, before concluding with the possible research gaps and future directions.
BibTeX
@inproceedings{ijcai2022p785,
title = {Vision-based Intention and Trajectory Prediction in Autonomous Vehicles: A Survey},
author = {Teeti, Izzeddin and Khan, Salman and Shahbaz, Ajmal and Bradley, Andrew and Cuzzolin, Fabio},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5630--5637},
year = {2022},
month = {7},
note = {Survey Track},
doi = {10.24963/ijcai.2022/785},
url = {https://doi.org/10.24963/ijcai.2022/785},
}