A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios

Sajjad Mozaffari, Eduardo Arnold, Mehrdad Dianati, Saber Fallah

2021

Abstract

Prediction of the manoeuvres of other vehicles can significantly improve the safety of automated driving systems. A manoeuvre prediction algorithm estimates the likelihood of a vehicle’s next manoeuvre using the motion history of the vehicle and its surrounding traffic. Several existing studies assume full observability of the surrounding traffic by utilising trajectory datasets collected by top-down view infrastructure cameras. However, in practice, automated vehicles observe the driving environment using egocentric perception sensors (i.e., onboard lidar or camera) which have limited sensing range and are subject to occlusions. This study firstly analyses the impact of these limitations on the performance of lane change prediction. To overcome these limitations, automated vehicles can cooperate in observing the environment by sharing their perception data through V2V communication. While it is intuitively expected that cooperation among vehicles can improve environment perception by individual vehicles, the other contribution of this work is to quantify the potential impacts of cooperation. To this end, we propose two perception models used to generate egocentric and cooperative perception dataset variants from a set of uniform scenarios in a benchmark dataset. This study can help system designers weigh the costs and benefits of alternative perception solutions for lane change prediction.

Paper Citation

in Harvard Style

Mozaffari S., Arnold E., Dianati M. and Fallah S. (2021). A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios. In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS, ISBN 978-989-758-537-1, pages 113-121. DOI: 10.5220/0010655700003061

in Bibtex Style

@conference{robovis21,
author={Sajjad Mozaffari and Eduardo Arnold and Mehrdad Dianati and Saber Fallah},
title={A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios},
booktitle={Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,},
year={2021},
pages={113-121},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010655700003061},
isbn={978-989-758-537-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems - Volume 1: ROBOVIS,
TI - A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios
SN - 978-989-758-537-1
AU - Mozaffari S.
AU - Arnold E.
AU - Dianati M.
AU - Fallah S.
PY - 2021
SP - 113
EP - 121
DO - 10.5220/0010655700003061