DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving

TL;DR

The paper introduces the DEV framework, a closed-loop system that dynamically adjusts vehicle automation levels based on real-time risk assessment, considering driver, environment, and vehicle interactions to improve safety and cooperation.

Contribution

It presents a novel risk-aware adaptive automation framework that models the interaction between driver, environment, and vehicle for safer driving automation.

Findings

Framework enables real-time risk assessment and adaptive automation adjustments.

Introduces a new nomenclature of indexes for core components affecting driving risk.

Supports smoother transitions and better cooperation between driver and automation.

Abstract

The increasing integration of automation in vehicles aims to enhance both safety and comfort, but it also introduces new risks, including driver disengagement, reduced situation awareness, and mode confusion. In this work, we propose the DEV framework, a closed-loop framework for risk-aware adaptive driving automation that captures the dynamic interplay between the driver, the environment, and the vehicle. The framework promotes to continuously adjusting the operational level of automation based on a risk management strategy. The real-time risk assessment supports smoother transitions and effective cooperation between the driver and the automation system. Furthermore, we introduce a nomenclature of indexes corresponding to each core component, namely driver involvement, environment complexity, and vehicle engagement, and discuss how their interaction influences driving risk. The DEV framework offers a comprehensive perspective to align multidisciplinary research efforts and guide the development of dynamic, risk-aware driving automation systems.