Authors:
Sajjad Mozaffari
1
;
Eduardo Arnold
1
;
Mehrdad Dianati
1
and
Saber Fallah
2
Affiliations:
1
Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, U.K.
;
2
Department of Mechanical Engineering Sciences, University of Surrey, Guildford, GU2 7XH, U.K.
Keyword(s):
Lane Change Prediction, Perception Models, Vehicle Behaviour Prediction, Intelligent Vehicles.
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 b
y 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.
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