Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Lim, Chee Peng, Mohamed, Shady, Nahavandi, Darius, Khosravi, Abbas, Nahavandi, Saeid, and Bhasin, Navneet (2021) A Fast and Reliable Approach for Driving Style Customization in Autonomous Vehicles. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 1869-1875. From: SMC 2021: IEEE International Conference on Systems, Man, and Cybernetics, 17-20 October 2021, Melbourne, VIC, Australia.

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Abstract

The usage of autonomous vehicles in the transportation sector can achieve the objective of a safe environment. To increase riding comfort in an autonomous vehicle, one main challenge is to implement motion scenarios according to the passenger’s driving behaviours. This leads to customization of the driving style of an autonomous vehicle according to the preference of its passenger. The main disadvantage of the current autonomous vehicles is the regeneration of driving motion signals without taking into consideration the comfort/discomfort of the passengers according to their driving behaviours and preferred driving styles such as acceleration/deceleration rate and steering styles. In this paper, a nonlinear autoregressive network model is developed and trained based on the generated motion scenarios of the passenger and the position of the autonomous vehicle, in order to predict and replicate the motion signals based on the passenger’s driving behaviours. The MATLAB toolbox is used to train the network and forecast the motion signals. The results show the usefulness of the proposed method in terms of a higher shape similarity level and a lower mean square error rate between the actual and forecasted motion signals. These regenerated motion signals can increase the riding comfort of autonomous vehicle’s passengers as it is able to imitate the behaviour of the passengers.

Item ID:	87051
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-4207-7
Copyright Information:	© 2021 IEEE.
Date Deposited:	27 Oct 2025 23:09
FoR Codes:	40 ENGINEERING > 4002 Automotive engineering > 400203 Automotive mechatronics and autonomous systems @ 70% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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