Driver Distraction and Reliance: Adaptive Cruise Control in the Context of Sensor Reliability and Algorithm Limits

This study investigated how system failures influenced drivers’reliance on Adaptive Cruise Control (ACC). A medium-fidelity driving simulatorwas used to evaluate the effect of driving condition (traffic, rain) and automation(manual control, ACC) on headway maintenance and brake response. Inconditions of rain, the signal continuity of the ACC sensors was degraded and inconditions of heavy traffic, the braking limits of the ACC system were exceeded.Dependent variables included response time to lead vehicle (LV) braking, numberof collisions, and both time headway (THW) and time-to-collision (TTC) atinstant of the brake response. Throughout the drive, a continuous (forced-paced)secondary task was introduced to determine how an in-vehicle task interactedwith ACC reliance. Results showed that the failure type influenced driver’sreliance on ACC with drivers relying more on ACC in traffic periods than in rainperiods. ACC appeared to offer a safety benefit when drivers were distracted withcomplex mental tasks in periods of heavy traffic

Considering the Human Across Levels of Automation: Implications for Reliance

This paper introduces human considerations that have yet to be fully addressed in industry standards for levels of automation. Currently-deployed vehicle automation is discussed according to these standards from a human interaction framing. The taxonomy-centric description of individual features provides insights into the challenges drivers may have in use of features in actual driving conditions. Initial data from an on-going naturalistic driving study of Tesla drivers is presented as a first-look at the prevalence of interaction challenges in real-world automation based on technology use. Implications for system design and training are discussed with the aim of centering industry and policy discussions on human-centric technology development

It’s All in the Timing: Using the Attend Algorithm to Assess Texting in the Nest Naturalistic Driving Database

To better understand cellular phone texting behavior and its relationship to crashing, we combined the sample-level glance data of NEST with the AttenD buffer algorithm to visualize glancing during texting within naturalistic epochs ending in crashes or no crashes. We found that texting periods were quite similar across the two, both in duration, number of individual texting tasks, and overall shape of the AttenD buffer curve. However, we found that crash epoch texting tended to occur closer to the onset of a crash event, and that texting during crashing may be initiated when the AttenD buffer level is lower (indicating depleted situation awareness), possibly due to prior or ongoing operational or secondary activities. We also made similar comparisons for radio interaction tasks, and found substantial differences between radio crash and baseline interactions. We conclude that whether a texting period ends in a crash may be dependent upon more than the individual differences in length of texting or amount of glancing. One’s level of situation awareness at the start of the activity (indicating a potential lack of judgment in picking up the device), in combination with a cascading losses of situation awareness that arise from the temporal pattern of on-road and off-road glances upstream from a safety-critical event, may be key predictive factors

Consumer Confusion with Levels of Vehicle Automation

A consumer-facing automation taxonomy is proposed to address emergent issues of consumer confusion related to automation types and associated role responsibility. A set of surveys were fielded to help understand the extent to which consumers were able to accurately interpret a proposed consumer-facing taxonomy relative to the 6-level SAE J3016 taxonomy. Results show a mixed benefit of the proposed set over the J3016 set. For both term types and definitions, consumers were best able to differentiate the extremes of automation types, leading to the question of whether or not it may be beneficial to provide a simplified representation to communicate functionality. A binary framing (“driving” vs. “riding”) in place of a 6-level taxonomy is proposed to ensure consumer understanding

Consumer Comfort with Vehicle Automation: Changes Over Time

Higher levels of vehicle automation are forecast as a potential mobility solution for many, but understanding consumer comfort and acceptance of selfdriving technologies remains an open question. Results from a series of surveys over three years showed a slight increase in the percentage of people comfortable with full self-driving automation in 2018, following a drop from 2016 to 2017. The recovery in comfort with higher levels of automation was most pronounced among younger adults between ages 25 and 44. However, the percentage of people only comfortable with no automation or features that activate only in certain situations such as in an emergency also increased in the past year, indicating a polarizing trend. Results from the survey also showed that acceptance of self-driving vehicles is conditional on people’s ability to drive as well as having assurance regarding the safety of the technology. Responses also point to a possible misunderstanding among the public regarding the definition and availability of full self-driving technology, indicating a need for improved messaging and consumer education

In the Context of Whole Trips: New Insights Into Driver Management of Attention and Tasks

It is becoming increasingly important to understand how drivers strategically manage tasks and thread attention across time, as they drive through varying situations and conditions -- and as they have the opportunity to delegate tasks to vehicle automation while taking up other tasks themselves. To develop an understanding of these higher-level driver behaviors requires a research focus on longer periods of driving -- even on “whole trip” driving. It may also require new tools and methods. Therefore, to explore insights and implications of a “whole trip” focus, data from 10 drivers were analyzed using methods tailored for identifying patterns within larger sequences of driving data than single-task epochs. The results are reported, discussed, and contrasted with more conventional approaches based on single-task epochs

Perceiving the Roadway in the Blink of an Eye-Rapid Perception of the Road Environment and Prediction of Events

This study investigated how quickly participants could develop a functional mental representation of a real-world road scene, based on briefly viewed recorded video. Using Amazon Mechanical Turk, we recruited 27 participants and collected 25k individual trials assessing the development of a percept of the road environment. This was operationalized as the duration of road video required for participants to predict which of two temporally spaced images would happen next. We found that participants could begin to build a representation of the road environment with as little as 100 ms of viewed road video and that the representation improved with additional video. These results suggest that drivers may begin to construct robust, predictive mental representations of the road environment with the briefest of glances, and the more information available to them, the more robust these representations are. While 100 ms of eyes-on-road time is insufficient to ensure safe driving, comprehension of the road environment begins in the blink of an eye