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Open AccessArticle

Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles

Graduate School of Science and Engineering, Doshisha University, Kyoto 602-8580, Japan
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Algorithms 2017, 10(4), 129; https://doi.org/10.3390/a10040129
Received: 30 September 2017 / Revised: 12 November 2017 / Accepted: 17 November 2017 / Published: 22 November 2017
We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of the dynamics of the accelerator pedal as the test set, the experimental results suggest that the evolved classifiers detect the emergency braking situation with at least 93% accuracy. The best performing classifier could be integrated into the agent that perceives the dynamics of the accelerator pedal in real time and—if emergency braking is detected—acts by applying full brakes well before the driver would have been able to apply them. View Full-Text
Keywords: emergency braking; driver-assisting agent; extreme gradient boosting; support vector machine; k-nearest neighbors; genetic algorithms emergency braking; driver-assisting agent; extreme gradient boosting; support vector machine; k-nearest neighbors; genetic algorithms
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Podusenko, A.; Nikulin, V.; Tanev, I.; Shimohara, K. Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles. Algorithms 2017, 10, 129.

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