Title: Recognition of driver emergency braking behaviour based on support vector machine optimised by memetic algorithm

Authors: Shenpei Zhou; Bingchen Qiao; Haoran Li; Bin Ran

Addresses: School of Automation, Wuhan University of Technology, Wuhan, Hubei, 430070, China ' School of Automation, Wuhan University of Technology, Wuhan, Hubei, 430070, China ' School of Automation, Wuhan University of Technology, Wuhan, Hubei, 430070, China ' Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA

Abstract: Surface electromyography (sEMG) is one of the main information sources of human motion detection and has been widely used. The lower limb sEMG signal is introduced into the recognition model of emergency braking behaviour, and the features from time domain, frequency domain and model parameters are extracted to construct a feature vector. The emergency driving behaviours are identified by using support vector machine (SVM), and a memetic algorithm (MA) based on particle swarm optimisation and hill climbing algorithm is proposed to optimise the parameters of SVM. The results show that the model has better classification performance than that without optimisation. The final recognition rate of emergency braking behaviour of same individual is up to 92.3%, and that of different individuals can reach 85.6%. Moreover, the system can detect emergency braking 220 ms earlier than operating brake pedal. At 100 km/h driving speed, the braking distance is reduced by 6.1 m.

Keywords: surface electromyography; sEMG; emergency braking; support vector machine; SVM; parameter optimisation; memetic algorithm; particle swarm optimisation; hill climbing algorithm; driving behaviours.

DOI: 10.1504/IJBIC.2020.112321

International Journal of Bio-Inspired Computation, 2020 Vol.16 No.4, pp.220 - 228

Received: 28 Jan 2019
Accepted: 05 Sep 2019
Published online: 12 Jan 2021 *

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