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A comparative study of ego-centric and cooperative perception for lane change prediction in highway driving scenarios
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Mozaffari, Sajjad, Arnold, Eduardo, Dianati, Mehrdad and Fallah, Saber (2021) A comparative study of ego-centric and cooperative perception for lane change prediction in highway driving scenarios. In: 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS, 27 - 28 Oct 2021. Published in: Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems, 1 pp. 113-121. ISBN 978-989-758-537-1. doi:10.5220/0010655700003061 [ 🗎 Public]. [ (🔓): Yes ].
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WRAP-A-comparative-study-of-ego-centric-and-cooperative-perception-for-lane-change-prediction-Mozaffari-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (816Kb) | Preview |
Official URL: https://doi.org/10.5220/0010655700003061
Abstract
Automated vehicles are required to predict the future manoeuvres, e.g., lane change, of their nearby vehicles to operate safely and efficiently. A Manoeuvre prediction algorithm estimates the likelihood of a target vehicle's next manoeuvre using the motion history of the target vehicle and its surrounding traffic. Several existing studies assume full observability of the surrounding traffic by utilising trajectory datasets collected by wide and top-down view infrastructure cameras. However, in practice, automated vehicles observe the driving environment using ego-centric perception sensors (i.e., onboard lidar or camera) which have limited sensing range and are subject to occlusion.
To overcome these limitations, automated vehicles can cooperate in observing the environment by sharing their perception data through V2V communication. This paper analyses the impact of ego-centric and cooperative perception on the lane change prediction problem. To this end, we propose two perception models used to generate egocentric and cooperative perception dataset variants from a widely used top-down view trajectory dataset. The evaluation results show that egocentric perception decreases the performance of our long-term lane change prediction model by 4% compared to the full observability mode in the original dataset. Furthermore, the results suggest that using cooperative perception with 20% penetration rate of automated vehicles significantly mitigate the performance loss caused by the limitations of egocentric perception.
| Item Type: | Conference Item (Paper) | ||||||
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| SWORD Depositor: | Library Publications Router | ||||||
| Journal or Publication Title: | Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems | ||||||
| Publisher: | SCITEPRESS - Science and Technology Publications | ||||||
| ISBN: | 978-989-758-537-1 | ||||||
| Official Date: | 2021 | ||||||
| Dates: |
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| Volume: | 1 | ||||||
| Page Range: | pp. 113-121 | ||||||
| DOI: | 10.5220/0010655700003061 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 30 March 2022 | ||||||
| Date of first compliant Open Access: | 30 March 2022 | ||||||
| Is Part Of: | 1 | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - ROBOVIS | ||||||
| Type of Event: | Conference | ||||||
| Date(s) of Event: | 27 - 28 Oct 2021 | ||||||
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