The Potential for Autonomous Vehicle Technologies to Address Barriers to Driving for Individuals with Autism

Individuals with autism represent a sizeable share of the U.S. population (almost 2%), and nearly half of those with autism have average to high levels of intelligence. However, available research shows that adults with autism have a much more difficult time becoming employed and living independently compared to both typically developing adults and adults with disabilities. This study reviews the available literature on the magnitude of challenges to driving and accessing essential opportunities for adults with autism, and the potential of autonomous vehicles to address those challenges. This study is unique in that it identifies the specific driving challenges and needs faced by those with autism. The study makes the following recommendations: (1) Occupational therapists certified for driving rehabilitation should evaluate the driving abilities of those with autism and provide enhanced driver training, with and without autonomous vehicle technologies (i.e., warning systems, steering, acceleration/deceleration, and braking systems) to address any driving challenges; (2) If autonomous vehicle technology is shown in (1) to be necessary to allow for safe driving, then public funding should be made available to help with its purchase, just as funding is currently made available for those with physical disabilities to modify vehicles with adaptive equipment; (3) More tests of high automation should be conducted to affordably expand transit access; however, in the interim, public funding should be made available to subsidize ride-hailing services when transit is not a feasible travel option for those with autism; and (4) More research is needed to evaluate the effectiveness of autonomous vehicle technology interventions for driving (in 1) and expanding transit access (in 3)

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl

Ambient hues and audible cues: An approach to automotive user interface design using multi-modal feedback

The use of touchscreen interfaces for in-vehicle information, entertainment, and for the control of comfort settings is proliferating. Moreover, using these interfaces requires the same visual and manual resources needed for safe driving. Guided by much of the prevalent research in the areas of the human visual system, attention, and multimodal redundancy the Hues and Cues design paradigm was developed to make touchscreen automotive user interfaces more suitable to use while driving. This paradigm was applied to a prototype of an automotive user interface and evaluated with respects to driver performance using the dual-task, Lane Change Test (LCT). Each level of the design paradigm was evaluated in light of possible gender differences. The results of the repeated measures experiment suggests that when compared to interfaces without both the Hues and the Cues paradigm applied, the Hues and Cues interface requires less mental effort to operate, is more usable, and is more preferred. However, the results differ in the degradation in driver performance with interfaces that only have visual feedback resulting in better task times and significant gender differences in the driving task with interfaces that only have auditory feedback. Overall, the results reported show that the presentation of multimodal feedback can be useful in design automotive interfaces, but must be flexible enough to account for individual differences

Priming to promote fluent motor skill execution: Exploring attentional demands

The effect of priming on the speed and accuracy of skilled performance and on a probe-reaction time task designed to measure residual attentional capacity, was assessed. Twenty-four skilled soccer players completed a dribbling task under three prime conditions (fluency, skill-focus, and neutral) and a control condition. Results revealed changes in trial completion time and secondary task performance in line with successfully priming autonomous and skill-focused attention. Retention test data for task completion time and probe-reaction time indicated a linear decrease in the priming effect such that the effect was nonsignificant after 30 min. Results provide further support for the efficacy of priming and provide the first evidence of concurrent changes in attentional demands, consistent with promoting or disrupting automatic skill execution

Effects of cognitive tasks on car drivers’ behaviors and physiological responses

The effects of drivers’ engagement in cognitive tasks (i.e., non-visual, cognitively loading activities unrelated to the task of driving) are debated and unclear. Numerous experiments show impaired driver behaviors, yet naturalistic studies typically do not find an increased crash risk. In the future, autonomous driving (AD) is expected to improve traffic safety while allowing safe engagement in cognitive (and other) tasks. Having the opportunity to perform non-driving related tasks while traveling may then motivate drivers to use AD, provided they can actually engage in the tasks. Unfortunately, research on drivers’ engagement in cognitive tasks suffers severe methodological limitations since reliable and unintrusive measures of cognitive load are lacking.The aim of this thesis is therefore to advance the understanding of task-induced cognitive load in the context of traffic safety. This aim is split into two objectives: A) to better understand how drivers’ involvement in cognitive tasks can affect safety-relevant driver behaviors and decisions and B) to provide methodological guidance about assessing cognitive load in drivers using physiological measures.To accomplish Objective A, effects of cognitive tasks on driver behaviors were studied during routine driving and in a safety-critical event in a driving simulator. Also, drivers’ ability to engage in a non-driving related task while using AD in real traffic was explored. In line with the cognitive control hypothesis (Engstr\uf6m et al., 2017), it was found that cognitive tasks negatively affected driver behaviors in situations where cognitive control was needed, for example in intersections—but not in a lead vehicle braking scenario where responses were triggered automatically by visual looming. It was also found that although the number of off-path glances decreased during cognitive load, the timing of the remaining glances was unaffected. Clearly, cognitive load has different effects on different mechanisms. When using AD, drivers were indeed capable of engaging in a non-driving related task—suggesting that AD will be able to fulfill drivers’ desire to perform such tasks while traveling, which may motivate AD usage and thus improve traffic safety (given that AD is truly safer than manual driving). Finally, a simulator study addressing Objective B showed that the measurability of cognitive load was greatly improved by recognizing that multiple coexisting mental responses give rise to different physiological responses. This approach can provide less context-dependent measurements and allows for a better, more detailed understanding of the effects of cognitive tasks.These findings can help improve traffic safety—both by being used in system development, and as part of the systems themselves

Attention and Task Engagement During Automated Driving

Many young drivers suffer fatal crashes each year in the United States at a rate approximately three times greater than more experienced drivers. Automated driving systems may serve to mitigate young drivers high crash rates but remain underexplored in research. This dissertation project examined the effects of levels of automation and interestingness of auditory clips on latent hazard anticipation in young drivers during simulated driving. Participants drove a vehicle at varying levels of vehicle automation (SAE Level 0, 2, or 3) in simulated scenarios, each containing a latent hazard event during which a boring, neutral, or interesting auditory clip was played. After completing all scenarios, participants completed an auditory stimuli recognition test and a questionnaire measuring the drivers’ calibration of their LHA performance. Results demonstrated that those in the L3 condition anticipated significantly fewer hazards than those in the L0 condition, corroborating previous research (Samuels et al., 2020). However, those in the L3 condition were also significantly poorer at anticipating latent hazards than those in the L2 condition, suggesting the importance of instruction on a drivers’ attentional allocation policy. A tradeoff was found between latent hazard anticipation and auditory recognition scores indicating the allocation of limited attentional resources as predicted by the Yamani and Horrey (2018) model. Interestingness of auditory stimuli had little to no effect on latent hazard anticipation. In general, automation may improve the multitasking ability of a young driver piloting L2 automation, but this benefit is lost for drivers of L3 automation. Instead, young drivers piloting L3 automation may anticipate latent hazards at rates as low as those observed in newly licensed drivers, and may be completely unaware of their failure to anticipate such hazards. The current research illustrates the criticality of user guidance when handling automated driving systems and serves as one step towards understanding the complex relationship between human drivers and automated systems

Warning a Distracted Driver: Smart Phone Applications, Informative Warnings and Automated Driving Take-Over Requests

abstract: While various collision warning studies in driving have been conducted, only a handful of studies have investigated the effectiveness of warnings with a distracted driver. Across four experiments, the present study aimed to understand the apparent gap in the literature of distracted drivers and warning effectiveness, specifically by studying various warnings presented to drivers while they were operating a smart phone. Experiment One attempted to understand which smart phone tasks, (text vs image) or (self-paced vs other-paced) are the most distracting to a driver. Experiment Two compared the effectiveness of different smartphone based applications (app’s) for mitigating driver distraction. Experiment Three investigated the effects of informative auditory and tactile warnings which were designed to convey directional information to a distracted driver (moving towards or away). Lastly, Experiment Four extended the research into the area of autonomous driving by investigating the effectiveness of different auditory take-over request signals. Novel to both Experiment Three and Four was that the warnings were delivered from the source of the distraction (i.e., by either the sound triggered at the smart phone location or through a vibration given on the wrist of the hand holding the smart phone). This warning placement was an attempt to break the driver’s attentional focus on their smart phone and understand how to best re-orient the driver in order to improve the driver’s situational awareness (SA). The overall goal was to explore these novel methods of improved SA so drivers may more quickly and appropriately respond to a critical event.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Multitasking while driving: a time use study of commuting knowledge workers to access current and future uses

Commuting has enormous impact on individuals, families, organizations, and society. Advances in vehicle automation may help workers employ the time spent commuting in productive work-tasks or wellbeing activities. To achieve this goal, however, we need to develop a deeper understanding of which work and personal activities are of value for commuting workers. In this paper we present results from an online time-use study of 400 knowledge workers who commute-by-driving. The data allow us to study multitasking-while-driving behavior of com-muting knowledge workers, identify which non-driving tasks knowledge workers currently engage in while driving, and the non-driving tasks individuals would like to engage in when using a safe highly automated vehicle in the future. We discuss the implications of our findings for the design of technology that supports work and wellbeing activities in automated cars

Analysis of autonomic indexes on drivers' workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditions

Advanced driver assistance systems (ADASs) allow information provision through visual, auditory, and haptic signals to achieve multidimensional goals of mobility. However, processing information from ADAS requires operating expenses of mental workload that drivers incur from their limited attentional resources. The change in driving condition can modulate drivers' workload and potentially impair drivers' interaction with ADAS. This paper shows how the measure of cardiac activity (heart rate and the indexes of autonomic nervous system (ANS)) could discriminate the influence of different driving conditions on drivers' workload associated with attentional resources engaged while driving with ADAS. Fourteen drivers performed a car-following task with visual ADAS in a simulated driving. Drivers' workload was manipulated in two driving conditions: one in monotonous condition (constant speed) and another in more active condition (variable speed). Results showed that drivers' workload was similarly affected, but the amount of attentional resources allocation was slightly distinct between both conditions. The analysis of main effect of time demonstrated that drivers' workload increased over time without the alterations in autonomic indexes regardless of driving condition. However, the main effect of driving condition produced a higher level of sympathetic activation on variable speed driving compared to driving with constant speed. Variable speed driving requires more adjustment of steering wheel movement (SWM) to maintain lane-keeping performance, which led to higher level of task involvement and increased task engagement. The proposed measures appear promising to help designing new adaptive working modalities for ADAS on the account of variation in driving condition