English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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