Coevolution of the Features of the Dynamics of the Accelerator Pedal and Hyperparameters of the Classifier for Emergency Braking Detection
Graduate School of Science and Engineering, Doshisha University, Kyoto 610-0321, Japan
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Actuators 2018, 7(3), 39; https://doi.org/10.3390/act7030039
Received: 26 June 2018 / Revised: 12 July 2018 / Accepted: 13 July 2018 / Published: 16 July 2018
(This article belongs to the Special Issue Novel Braking Control Systems)
We investigate the feasibility of inferring the intention of the human driver of road motor vehicles to apply emergency braking solely by analyzing the dynamics of lifting the accelerator pedal. Focusing on building the system that reliably classifies the emergency braking situations, we employed evolutionary algorithms (EA) to coevolve both (i) the set of features that optimally characterize the movement of accelerator pedal and (ii) the values of the hyperparameters of the classifier. The experimental results demonstrate the superiority of the coevolutionary approach over the analogical approaches that rely on an a priori defined set of features and values of hyperparameters. By using simultaneous evolution of both features and hyperparameters, the learned classifier inferred the emergency braking situations in previously unforeseen dynamics of the accelerator pedal with an accuracy of about 95%. We consider the obtained results as a step towards the development of a brake-assisting system, which would perceive the dynamics of the accelerator pedal in a real-time and in case of a foreseen emergency braking situation, would apply the brakes automatically well before the human driver would have been able to apply them.
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Keywords:
emergency braking system; cooperative coevolution; evolutionary computation; driving assisting agent; extreme gradient boosting
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MDPI and ACS Style
Podusenko, A.; Nikulin, V.; Tanev, I.; Shimohara, K. Coevolution of the Features of the Dynamics of the Accelerator Pedal and Hyperparameters of the Classifier for Emergency Braking Detection. Actuators 2018, 7, 39.
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