Dynamic characteristics of commercial Adaptive Cruise Control across driving situations: Response time, string stability, and asymmetric behavior

Abstract

Adaptive Cruise Control (ACC) is a popular feature for long-distance highway driving due to its convenience. Research has been conducted on the driving characteristics of commercial ACC vehicles that could impact road capacity and congestion. While response time and string stability are major characteristics, previous studies have often overlooked their variations across driving situations. This study analyzes the dynamic characteristics of commercial ACC, including response time, string stability, and asymmetric behavior across different driving situations. A method is proposed to extract the response time of commercial ACC vehicles during cruising, decelerating, and accelerating situations, using cross-correlation and acceleration threshold methods. Phenomena that influence string stability are categorized based on driving situations focusing on their origin and features. This study identifies patterns in asymmetric behavior and presents a car-following model calibration process that incorporates observed features using the OpenACC dataset. The findings reveal distinct variations in response time across different driving situations, escalating in the sequence of deceleration, cruising, and acceleration. String instability during deceleration is influenced by the vehicle’s response time, while during acceleration, it stems from an expanded gap reduction process. ACC vehicles exhibit asymmetric behavior, with a reduced tolerance for gap changes. The Helly model, which integrates response times, asymmetric behavior, and maximum acceleration, accurately simulates vehicle movement and string instability. The observed variations in response time and asymmetric behavior across driving situations provide an understanding of the traffic hysteresis of commercial ACC vehicles. Furthermore, our analysis suggests that achieving string stability requires diverse approaches for each driving situation.