Predicting driver takeover performance in conditionally automated driving.

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.

Perceived safety benefits, concerns, and utility of advanced driver assistance systems among owners of ADAS-equipped vehicles.

OBJECTIVE: There are many unknowns regarding drivers' use and acceptance of advanced vehicle technologies. This research aimed to examine drivers' perceptions of advanced driver assistance systems (ADAS). METHODS: This research was conducted using structured interviews and focus groups of owners of vehicles with advanced technologies. RESULTS: Drivers' perceptions about ADAS were mixed, but generally safety was considered to be the greatest value of the systems. There was recognition that the systems may result in overreliance and thus encourage distraction behaviors or other bad driving habits, and participants generally expressed that they were ultimately responsible for the vehicle's operation and needed to be ready to override the system if it failed. CONCLUSIONS: The findings indicate that driver characteristics and individual factors may influence perceptions, behaviors, and interactions with safety technology, and this research is a first step toward understanding any influences. Human factors issues related to automated vehicle technologies are critical for design and deployment, including those of trust, acceptance, and understanding of systems.