Multidimensional Assessment of Takeover Performance in Conditionally Automated Driving

2025-07-29

AI Summary

In conditional automated driving, human–machine control transitions following takeover requests pose safety risks due to response latency and suboptimal maneuver quality. This study employs a driving simulator experiment coupled with an XGBoost predictive model to quantitatively assess takeover performance across three dimensions: response efficiency, user experience, and driving safety. It systematically disentangles the distinct roles of Situation Awareness (SA) and Residual Capacity (RC). Results indicate that SA primarily accelerates takeover response time, whereas RC exerts stronger influence on subjective comfort, trajectory stability, and overall safety—demonstrating complementarity, not substitutability, between the two constructs. Compared to a baseline model relying solely on driver demographic and behavioral features, incorporating SA and RC significantly improves prediction accuracy across all multidimensional takeover metrics. These findings provide novel theoretical insights and empirical evidence for optimizing human–vehicle collaborative interaction and designing adaptive takeover assistance strategies.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

When automated driving systems encounter complex situations beyond their operational capabilities, they issue takeover requests, prompting drivers to resume vehicle control and return to the driving loop as a critical safety backup. However, this control transition places significant demands on drivers, requiring them to promptly respond to takeover requests while executing high-quality interventions. To ensure safe and comfortable control transitions, it is essential to develop a deep understanding of the key factors influencing various takeover performance aspects. This study evaluates drivers' takeover performance across three dimensions: response efficiency, user experience, and driving safety - using a driving simulator experiment. EXtreme Gradient Boosting (XGBoost) models are used to investigate the contributions of two critical factors, i.e., Situational Awareness (SA) and Spare Capacity (SC), in predicting various takeover performance metrics by comparing the predictive results to the baseline models that rely solely on basic Driver Characteristics (DC). The results reveal that (i) higher SA enables drivers to respond to takeover requests more quickly, particularly for reflexive responses; and (ii) SC shows a greater overall impact on takeover quality than SA, where higher SC generally leads to enhanced subjective rating scores and objective execution trajectories. These findings highlight the distinct yet complementary roles of SA and SC in shaping performance components, offering valuable insights for optimizing human-vehicle interactions and enhancing automated driving system design.

Problem

• Evaluates driver takeover performance in automated driving transitions
• Investigates impact of Situational Awareness and Spare Capacity on safety
• Optimizes human-vehicle interaction for safer automated driving systems

Innovation

• XGBoost models predict takeover performance metrics
• Situational Awareness improves reflexive response speed
• Spare Capacity enhances takeover quality significantly