Prediction of emergency braking based on the pattern of lifting motion of accelerator pedal

Podusenko, Albert; Nikulin, Vsevolod; Tanev, Ivan; Shimohara, Katsunori

doi:10.23919/sice.2017.8105641

Cited by 3 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classifier with the proposed in the research of Haufe et al [10]. Another possible solution of the EBC problem could be based on the analysis of time series of the position of the accelerator pedal [11]. As shown in Figure 1, the specific motion of the accelerator pedal corresponds to a specific driving situation-such as accelerating, cruising, slowing down, normal braking, and emergency braking.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

Driver’s Foot Trajectory Tracking for Safe Maneuverability Using New Modified reLU-BiLSTM Deep Neural Network

Ansari

Naghdy

2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

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

Podusenko¹,

Nikulin²,

Tanev³

et al. 2017

Algorithms

View full text Add to dashboard Cite

Abstract: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.

show abstract

Prediction of emergency braking based on the pattern of lifting motion of accelerator pedal

Cited by 3 publications

References 6 publications

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

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

Driver’s Foot Trajectory Tracking for Safe Maneuverability Using New Modified reLU-BiLSTM Deep Neural Network

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

Contact Info

Product

Resources

About