Adaptive Time Budgets for Safe and Comfortable Vehicle Control Transition in Conditionally Automated Driving

Abstract

Conditionally automated driving requires drivers to resume vehicle control promptly when automation reaches its operational limits. Ensuring smooth vehicle control transitions is critical for the safety and efficiency of mixed-traffic transportation systems, where complex interactions and variable traffic behaviors pose additional challenges. This study addresses this challenge by introducing an adaptive time budget framework that provides drivers with sufficient time to complete takeovers both safely and comfortably across diverse scenarios. We focus in particular on the takeover buffer, that is, the extra time available after drivers consciously resume control to complete evasive maneuvers. A driving simulator experiment is conducted to evaluate the influence of different takeover buffer lengths on safety-related indicators (minimum time-to-collision, maximum deceleration, and steering wheel angle) and subjective assessments (perceived time sufficiency, perceived risk, and performance satisfaction). Results show that (i) takeover buffers of about 5-6 seconds consistently lead to optimal safety and comfort; and (ii) drivers prefer relatively stable takeover buffers across varying traffic densities and n-back tasks. This study introduces an adaptive time budget framework that dynamically allocates transition time by incorporating a predicted takeover time and a preferred takeover buffer (piecewise function). This can serve as an important first step toward providing drivers with sufficient time to resume vehicle control across diverse scenarios, which needs to be validated in more diverse and real-world driving contexts. By aligning the provided time budget with driver needs under specific circumstances, the adaptive framework can improve reliability of control transitions, facilitate human-centered automated driving, reduce crash risk, and maintain overall traffic efficiency.