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Different in-vehicle interface designs have been proposed to support drivers in operating vehicle automation systems. Measures of visual attention are particularly useful in the evaluation of such interfaces, especially in conditions when driving performance measures cannot be used, i.e., when the driver is not physically controlling the vehicle. In this paper, we present a literature review on visual attention measures in the assessment of in-vehicle interfaces to support the use of driving automation. This review was a preliminary pilot study for a larger, ongoing scoping review investigating visual attention and driving automation more broadly. Based on a systematic search of one engineering literature database, this review focused on driving automation that does not require the driver to physically control the vehicle but does require driver involvement in some capacity. The findings of this review contributed to the development of the larger review and can help identify future research trends.

This paper presents an adaptive mode (level) transition in highly combined driving automation in which the mode of a system could adaptively shift to any level including SAE level 3 (conditional automation, CA) to level 2 (partial automation) based on the driving environment. We show the effects of the adaptive transition on the take over of car control by a human driver and driving behavior after intervention when the system issues a response to intervene. A driving simulator experiment is conducted to collect data during the transition from automated control to manual driving in three scenes: obstacle on a driving lane, blurred lane mark, and stopped car ahead. Results indicate that the interventions of drivers who experience the adaptive transition are delayed in comparison to those who experience only the fixed transition. The adaptive transition is conducive for drivers to stop the car for preventing a potential collision with a stopped car ahead. Owing to the adaptive transition, drivers perceive a critical hazard after taking over car control and provide a rapid response. In addition, during the adaptive transition, drivers prefer verbal messages to the simple “beeping” message.

Little is known about the use of driving automation in production vehicles. This study measured mileage driven on different roadway classes when level 1 and 2 automation was engaged. Volunteer drivers drove for 4 weeks in a Range Rover Evoque with adaptive cruise control (ACC) or a Volvo S90 with ACC and Pilot Assist (PA) with one of two versions of PA software. The highest percentage of mileage driven when the automation was on/active (39.5%) was on interstates, other freeways, and expressways. The Evoque’s ACC was engaged for a higher percentage of miles than the S90’s ACC or PA. The S90’s software change was associated with a doubling of mileage driven on interstates, other freeways, and expressways when PA was on/active. The study identified potential misuse with some drivers engaging automation for relatively high percentages of mileage on road classes where owner manuals indicate use may be inappropriate.

Advanced driver-assistance systems and partial driving automation are becoming increasingly common, yet despite their growing prevalence, drivers seem to know very little about them. Previous studies have found that owners of ADAS equipped vehicles have demonstrated misperceptions or lack of awareness about system limitations, which may impact driver comfort with and reliance on these systems. The purpose of this study was to determine the effectiveness of two training strategies on drivers’ knowledge and perceived familiarity of vehicle automation as well as their environment monitoring behaviors during system use. Forty volunteers participated in a multi-stage research study in which they were exposed to either a conventional training protocol, self-learning through the owner’s manual, or an experimental (multimedia) training protocol, using the in-vehicle display technologies as training tools. Results indicate training strategy elicits limited differences in knowledge and no difference in driver behaviors or attitudes. Behaviors and attitudes were heavily influenced by time and experience with the driving automation system while knowledge of the vehicle systems remained unchanged.

Little is known about how consumers interact with driving automation technology that controls steering, speed, or headway in production vehicles. Forty-eight Insurance Institute for Highway Safety employees used a Honda Civic, Infiniti QX60, Toyota Prius, or Audi A4 or Q7 as a personal vehicle for up to several weeks and completed surveys about their experiences. Agreement about whether adaptive cruise control (ACC) or active lane keeping (ALK) improved driving experience varied significantly among vehicles. The Q7’s ACC improved the driving experience significantly more than its ALK. The Civic’s ALK improved the driving experience more than the Q7’s system, but this effect only approached significance. Drivers were most comfortable using systems on free-flowing interstates and least comfortable using ACC in stop-and-go traffic and ALK on curvy roads. The findings show a range of qualitative differences in driving automation technologies and that use of current technologies likely is limited to low-demand conditions.

Background: Automated driving systems (ADSs) have the potential to fundamentally transform transportation by reducing crashes, congestion, and cost while improving traffic efficiency and access to mobility for the transportation-challenged population (US DOT, 2020). However, a recent on-road test of five vehicles capable of SAE Level 2 (SAE, 2016) driving automation equipped with forward collision warning (FCW), adaptive cruise control (ACC), lane departure warning (LDW), and lane keep assist (LKA) revealed that ADSs may not work as expected in typical driving situations, such as approaching stopped vehicles and negotiating hills and curves (IIHS, 2018). Even worse, people may not use ADSs as intended due to their misunderstanding of, over-trust, or distrust in such systems’ capabilities and limitations (IIHS, 2019). As Level 2 ADSs have become commercially available, accounts of unintended uses of these systems and fatal consequences have emerged. For example, a recent news article reported a Tesla driver napping behind the wheel (Fox News, 2019). Objective: Given the growing availability of ADSs on public roadways as well as the risk of their unintended use and safety consequences, this work aimed to better understand (1) realworld use of ADSs, (2) prevalence of unintended use of such systems, and (3) driver impressions after prolonged use of such systems. Method: The research team investigated an existing naturalistic driving database collected from the Virginia Connected Corridor Level 2 Naturalistic Driving Study (VCC L2 NDS, Dunn, Dingus, & Soccolich, 2019). The dataset contains data from 50 participants who drove personally owned vehicles for 12 months. Participating vehicles were equipped with longitudinal control systems (e.g., ACC) at the minimum, although most also had lateral control systems (e.g., LKA). Specifically, we investigated safety-critical events (SCEs, Guo & Fang, 2013) of different severity levels (e.g., crashes and near-crashes) captured in the data. We also examined drivers’ responses to a post-study questionnaire that captured drivers’ subjective ratings on the usefulness and usability of the ADS. Results: We found that 47 out of 235 (20%) SCEs involved ADS use. An in-depth analysis of 47 SCEs revealed that people misused ADSs in 57% of SCEs (e.g., engaged in secondary tasks, used the systems not on highways, or with hands off the wheel). During 13% of SCEs, the systems neither reacted to the situation nor warned the driver. A post-study survey showed that people found ADSs useful and usable. However, the more participants were positive to ADS features, the more they felt comfortable engaging in secondary tasks, which is an unintended side effect of Level 2 ADSs as they require the human driver’s supervision. This study also captured some scenarios where the ADSs did not meet driver expectations. Many people reported that the longitudinal control features did not respond well to cutting-in leads (23% of participants) and stopped leads (14%). The lateral control features were often automatically disengaged when encountering blurred lane markings (14% of participants reported) and had difficulties when negotiating curves (9%). Conclusion: This study contributes to a better understanding of the capabilities and limitations of early production SAE L2 vehicles, the prevalence of the unintended use of ADSs, and drivers’ perceptions of these new technologies. Designers of human-machine interfaces (HMIs) for such systems should always consider the possibility of drivers’ overconfidence in the systems. Therefore, it might be better for vehicles to have multimodal HMIs (Large et al., 2019) adaptive to not only the urgency of situations but also driver state by monitoring driver behavior and engagement in the primary task of driving. Application: The findings from this study may inform the development of HMIs, training programs, and owner’s manuals to reduce the unintended use of ADSs and safety consequences. The identified characteristics of situations where the ADSs failed to warn drivers during SCEs will further inform the development of testing scenarios to ensure ADS safety.