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.
Methodological Approach towards Evaluating the Effects of Non-Driving Related Tasks during Partially Automated Driving