Accelerated Failure Time Model to Explore the Perception Response Times of Drivers in Simulated Car-Following Scenarios

Abstract

In the development of effective rear-end collision alarm systems, understanding the factors that influence the perception response times (PRT) of drivers is important for the design of a reasonable lead time for the warning (or intervention) of likely collisions. Previous studies have proposed different approaches for examining the impact of situational or individual factors on the PRT of drivers. However, unobserved heterogeneity has not been considered and neither has a duration-modeling approach been used, resulting in a lack of accurate estimation. The purpose of the present study was to explore the effect of the driving situation and individual differences on the PRT of drivers while also considering unobserved heterogeneity. A total of 101 participants were exposed to different levels of secondarily cognitive load and situational urgency in simulated d scenarios. Several accelerated failure time (AFT) duration models, both with and without heterogeneity, were developed to model the PRT of drivers, while factors related to driving situation and individual differences were incorporated. The results indicate that influential factors include age, working memory capacity (WMC), cognitive load, and initial time headway exerted significant effects on the PRT of drivers. The hazard rate changed by 14.4%, 22.6%, and 7.5% when age, cognitive load, and initial time headway changed by one unit, respectively. Furthermore, the hazard rate decreases by more than 20% for individuals with higher WMC compared with baseline individuals. These results suggest that the AFT model that considers unobserved heterogeneity can provide a more accurate estimation of the PRT compared to other duration models. These findings can be expected to provide a further understanding of drivers’ braking behaviors, which will contribute to the development of advanced driving assistant systems as well as safety assessments of in-vehicle information and communication technologies.

Multi-State Car-Following Behavior Simulation in a Mixed Traffic Flow for ICVs and MDVs

“…Drivers tend to choose acceptable strategies rather than optimal strategies due to insufficient experience or time. Current car-following models that consider human factors can be divided into models that consider perception thresholds [6,7], a driver's visual angles [8,9], risk perception [10,11], and distraction and errors [12,13]. For example, Ozkan et al [14] used inverse reinforcement learning to model the unique car-following behaviors of different human drivers when interacting with CAVs and other human-driven vehicles.…”

How Do Cognitive Interventions Impact Driver Aggressiveness in China?—A Driving Simulator Study

“…In this respect, scholars mainly adopt two approaches to reduce aggressive driving behaviors of young drivers: the indirect intervention method through improving the inhibitory control ability and the direct intervention method through cognitive intervention. Tis method of reducing risky driving behaviors by improving cognitive control ability is based on the assumption that poor impulse control ability is the main factor leading to risky driving behaviors of young drivers [5,[11][12][13][14]. Multiple scholars have studied this topic, but as the studies on the neural mechanism of cognitive control are still in their infancy, little is known about whether this function is improvable through training and whether the underlying brain neural network could be remodeled [15].…”