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Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways

Arbabi, Salar, Dixit, Shilp, Zheng, Ziyao, Oxtoby, David, Mouzakitis, Alexandros and Fallah, Saber (2020) Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways In: 2020 IEEE 92nd Vehicular Technology Conference: VTC2020-Fall, 4-7 Oct 2020, Victoria, British Columbia, Canada.

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Quantifying and encoding occupants’ preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with safety constraints. It is shown that the decision model can replicate human drivers’ discretionary lane-change decisions with up to 92% accuracy. Further proof of concept simulation of an overtaking manoeuvre is shown, whereby the actions of the simulated vehicle are logged while the dynamic environment evolves as per ground truth data recordings.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Authors :
Dixit, Shilp
Zheng, Ziyao
Oxtoby, David
Mouzakitis, Alexandros
Date : 14 July 2020
Funders : Jaguar Land Rover, UK-EPSRC
Grant Title : Jaguar Land Rover
Uncontrolled Keywords : Autonomous Vehicles; Decision Making; Motion Planning
Additional Information : Embargo OK Metadata pending
Depositing User : James Marshall
Date Deposited : 24 Jul 2020 13:05
Last Modified : 27 Jul 2020 09:09

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