Coevolution of the Features of the Dynamics of the Accelerator Pedal and Hyperparameters of the Classifier for Emergency Braking Detection

Albert Podusenko,Ivan Tanev,Vsevolod Nikulin,Katsunori Shimohara

doi:10.3390/act7030039

Abstract

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.

Highlights

In recent years, the technological growth of the intellectual driving aid cannot be overlooked [1].The interest in this field is dictated by the needs of increasing the safety of the road traffic
In our work [13] we proposed a method of applying genetic algorithms (GA) for (i) tuning the hyper-parameters and (ii) selecting the best combinations of manually extracted features of the time series of the accelerator pedal in order to increase the quality of classifiers
The coevolution of the features and the classifier implies that the overall computational overhead of the real-world implementation of the brake assisting system would consist of two components corresponding to the following two classification stages: (i) calculating the values of the features in accordance with the evolved algebraic expression, and (ii) classifying the braking situations from the calculated features by the XGBoost classifier

Summary

Introduction

The technological growth of the intellectual driving aid cannot be overlooked [1] The interest in this field is dictated by the needs of increasing the safety of the road traffic. 1.3 million people die in road crashes every year [2] Part of these tragedies is caused by too-late brake application that could be prevented by embedding automated brake assistance into the cars. The automated braking system is considered to be beneficial in many traffic situations This type of aid suffers from serious engineering and psychological problems. One of these problems—according to risk homeostasis theory [7]—is overconfidence in the fully automated driving aids, which, in turn, could possibly blunt the human driver and lead to dangerous situations on the roads. For implementing the brake assistance, the emergency braking classification (EBC) problem [8] must be solved

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Discussion

Conclusion