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

2025-08-14

AI Summary

Critical safety issues in automated driving—including driver disengagement, degraded situation awareness, and mode confusion—undermine human–machine collaboration. Method: This study proposes a closed-loop “driver–environment–vehicle” co-adaptation framework. It introduces quantitative metrics for driver engagement, environmental complexity, and vehicle intervention level, and designs a real-time risk-aware dynamic automation-level adjustment mechanism. By integrating driving behavior analysis, multi-source environmental perception, and closed-loop feedback control, the framework enables smooth control transitions and rapid risk response. Contribution/Results: Experiments demonstrate that the approach significantly reduces human error and mode confusion probability, while enhancing collaborative safety, system reliability, and takeover smoothness. It provides a scalable methodological foundation for human-centered adaptive driving automation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Addressing driver disengagement and reduced situation awareness in automation
• Developing dynamic risk-aware adaptive driving automation framework
• Aligning multidisciplinary research for safer human-automation cooperation

Innovation

• Closed-loop framework for risk-aware automation
• Real-time risk assessment for smoother transitions
• Dynamic adjustment of automation levels