Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios

TL;DR

This paper introduces a human-based risk model for driver support that leverages driver perception and personalization to provide earlier warnings and reduce errors in interactive driving scenarios.

Contribution

It presents a novel risk model combining driver perception and personalization, improving warning timeliness and accuracy over existing models.

Findings

Earlier warning times achieved

Reduced warning errors demonstrated

Effective in multiple interactive scenarios

Abstract

This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model not using human driver information.