Evaluating secondary input devices to support an automotive touchscreen HMI: a cross-cultural simulator study conducted in the UK and China

Touchscreen Human-Machine Interfaces (HMIs) are a well-established and popular choice to provide the primary control interface between driver and vehicle, yet inherently demand some visual attention. Employing a secondary device with the touchscreen may reduce the demand but there is some debate about which device is most suitable, with current manufacturers favouring different solutions and applying these internationally. We present an empirical driving simulator study, conducted in the UK and China, in which 48 participants undertook typical in-vehicle tasks utilising either a touchscreen, rotary-controller, steering-wheel-controls or touchpad. In both the UK and China, the touchscreen was the most preferred/least demanding to use, and the touchpad least preferred/most demanding, whereas the rotary-controller was generally favoured by UK drivers and steering-wheel-controls were more popular in China. Chinese drivers were more excited by the novelty of the technology, and spent more time attending to the devices while driving, leading to an increase in off-road glance time and a corresponding detriment to vehicle control. Even so, Chinese drivers rated devices as easier-to-use while driving, and felt that they interfered less with their driving performance, compared to their UK counterparts. Results suggest that the most effective solution (to maximise performance/acceptance, while minimising visual demand) is to maintain the touchscreen as the primary control interface (e.g. for top-level tasks), and supplement this with a secondary device that is only enabled for certain actions; moreover, different devices may be employed in different cultural markets. Further work is required to explore these recommendations in greater depth (e.g. during extended or real-world testing), and to validate the findings and approach in other cultural contexts

Human factor guidelines for the design of safe in-car traffic information services

The first version of the “Human factor guidelines for the design of safe in-car traffic information services” was compiled in 2014. In 2016 the guidelines were updated by Connecting Mobility/ DITCM, and the present version is a further update of that version. New systems have been introduced into the marked, and the role of apps on smartphones has increased. This report was updated to include recent developments such as gesture control. The guidelines are aimed at in-car traffic information services

Driver Acceptance of Advanced Driver Assistance Systems and Semi-Autonomous Driving Systems

Advanced Driver Assistance Systems (ADAS) and semi-autonomous driving systems are intended to enhance driver performance and improve transportation safety. The potential benefits of these technologies, such as reduction in number of crashes, enhancing driver comfort or convenience, decreasing environmental impact, etc., are well accepted and endorsed by transportation safety researchers and federal transportation agencies. Even though these systems afford safety advantages, they challenge the traditional role of drivers in operating vehicles. Driver acceptance, therefore, is essential for the implementation of ADAS and semi-autonomous driving systems into the transportation system. These technologies will not achieve their potential if drivers do not accept them and use them in a sustainable and appropriate manner. The potential benefits of these in-vehicle assistive systems presents a strong need for research. A comprehensive review of current literature on the definitions of acceptance, acceptance modelling approaches, and assessment techniques was carried out to explore and summarize the different approaches adopted by previous researchers. The review identified three major research needs: a comprehensive evaluation of general technology acceptance models in the context of ADAS, development of an acceptance model specifically for ADAS and similar technologies, and development of an acceptance assessment questionnaire. Two studies were conducted to address these needs. In the first study, data collection was done using two approaches: a driving simulator approach and an online survey approach. In both approaches, participants were exposed to an ADAS and, based on their experience, responded to several survey questions to indicate their attitude toward using the ADAS and their perception of its usefulness, usability, reliability, etc. The results of the first study showed the utility of the general technology acceptance theories to model driver acceptance. A Unified Model of Driver Acceptance (UMDA) and two versions (a long version with 21 items and a short version with 13 items) of an acceptance assessment questionnaire were also developed, based on the results of the first study. The second was conducted to validate the findings of first study. The results of the second study found statistical evidence validating UMDA and the two versions of the acceptance assessment questionnaire

Scenario Modeling and Execution for Simulation Testing of Automated-Driving Systems

Automated Driving Systems (ADS) have the potential to significantly impact the future of ground mobility. However, safety assurance is still a major obstacle. Field testing alone is impractical and simulation is required to scale and accelerate testing. Further, it covers difficult and rare cases that are too risky to be performed on the closed-course. Evaluating a wide range of operating scenarios in simulation is essential to ensure ADS safety, reliability, and conformity to traffic regulations as the level of automation increases. In order to achieve this goal, Scenario-based testing for ADS must be able to model and simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for high-level scenario modelling have been proposed, they lack the features to precisely and reliably control the required micro-simulation, while also supporting behavior reuse and test reproducibility for a wide range of interactive scenarios. To fill this gap between scenario design and execution, this thesis proposes a Domain- Specific Language (DSL) for scenario representation, and a model for vehicle behavior in scenario design and simulation. The main research goal is to improve scenario modeling and execution for ADS testing in simulation, contributing to safety assurance in ADS development. First, we present the language GeoScenario to help researchers and engineers to develop tool-independent test scenarios, migrate scenarios between tools, and to evaluate their systems under alternative testing environments. The language is built on top of the well-known Open Street Map standard, and designed to be lightweight and extensible. Second, we propose the Simulated Driver-Vehicle Model (SDV) to represent and simulate vehicles as dynamic entities with their behavior being constrained by scenario design and goals set by testers. This model combines driver and vehicle as a single entity. It is based on human-like driving and the mechanical limitations of real vehicles for realistic simulation. The layered architecture of the model leverages behavior trees to express high-level behaviors in terms of lower-level maneuvers, affording multiple driving styles and reuse. Further, optimization-based maneuver planner guides the simulated vehicles towards the desired behavior. Finally, our extensive evaluation shows the language and model’s design effectiveness using NHTSA pre-crash scenarios, its motion realism in comparison to naturalistic urban traffic, and its scalability with traffic density. We show the applicability of SDV model to test a real ADS and to identify crash scenarios, which are impractical to represent using predefined vehicle trajectories. The SDV model instances can be injected into existing simulation environments via co-simulation

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

Clarity of View: An Analytic Hierarchy Process (AHP)-Based Multi-Factor Evaluation Framework for Driver Awareness Systems in Heavy Vehicles

Several emerging technologies hold great promise to improve the situational awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. The average commercial vehicle driver in the USA is 54 years old with many drivers continuing past retirement age. Current methods for evaluating visibility systems only consider field of view and do not incorporate measures of the cognitive elements critical to drivers, especially the older demographic. As a result, industry is challenged to evaluate new technologies in a way that provides enough information to make informed selection and purchase decisions. To address this problem, we introduce a new multi-factor evaluation framework, “Clarity of View,” that incorporates several important factors for visibility systems including: field of view, image detection time, distortion, glare discomfort, cost, reliability, and gap acceptance accuracy. It employs a unique application of the Analytic Hierarchy Process (AHP) that involves both expert participants acting in a Supra-Decision Maker role alongside driver-level participants giving both actual performance data as well as subjective preference feedback. Both subjective and objective measures have been incorporated into this multi-factor decision-making model that will help industry make better technology selections involving complex variables. A series of experiments have been performed to illustrate the usefulness of this framework that can be expanded to many types of automotive user-interface technology selection challenges. A unique commercial-vehicle driving simulator apparatus was developed that provides a dynamic, 360-degree, naturalistic driving environment for the evaluation of rearview visibility systems. Evaluations were performed both in the simulator and on the track. Test participants included trucking industry leadership and commercially licensed drivers with experience ranging from 1 to 40 years. Conclusions indicated that aspheric style mirrors have significant viability in the commercial vehicle market. Prior research on aspheric mirrors left questions regarding potential user adaptation, and the Clarity of View framework provides the necessary tools to reconcile that gap. Results obtained using the new Clarity of View framework were significantly different than that which would have previously been available using current industry status-quo published test methods. Additional conclusions indicated that middle-aged drivers performed better in terms of image detection time than young and elderly age categories. Experienced drivers performed better than inexperienced drivers, regardless of age. This is an important conclusion given the demographic challenges faced by the commercial vehicle industry today that is suffering a shortage of new drivers and may be seeking ways to retain its aging driver workforce. The Clarity of View evaluation framework aggregates multiple factors critical to driver visibility system effectiveness into a single selection framework that is useful for industry. It is unique both in its multi-factor approach and custom-developed apparatus, but also in its novel approach to the application of the AHP methodology. It has shown significance in ability to discern more well-informed technology selections and is flexible to expand its application toward many different types of driver interface evaluations

ALCOHOL-INDUCED IMPAIRMENT OF SIMULATED DRIVING PERFORMANCE AND BEHAVIORAL IMPULSIVITY IN DUI OFFENDERS

Licensed drivers arrested for driving under the influence (DUI) of alcohol have increased rates of vehicle crashes, moving violations, traffic tickets, and contribute to an estimated 120 million occurrences of impaired driving per year (Evans, 2004; Jewett et al., 2015). Survey research on DUI offenders indicates traits of impulsivity (e.g., sensation seeking). Together, these pieces of evidence suggest that DUI offenders display patterns of impulsive action and risk-taking while driving. However, to-date DUI offenders are rarely studied in a laboratory setting, and not much is known about how they respond to a dose of alcohol. The present study examined the degree to which DUI offenders display an increased sensitivity to the acute impairing effects of alcohol on mechanisms of behavioral impulsivity, skill and risk-based driving simulations, and subjective evaluations of driving fitness and perceived intoxication following alcohol consumption. A sample of 20 DUI offenders were compared to a demographically-matched sample of 20 control drivers. All participants attended two dose sessions in which they received either a 0.65 g/kg dose of alcohol or a placebo dose, counterbalanced, on separate days. Results indicated that alcohol affected all of the behavioral outcome measures. More specifically, alcohol increased impulsive choice responses and decreased response inhibition on the behavioral impulsivity tasks. Alcohol also increased risky driving behaviors and decreased driving-related skills. Furthermore, alcohol generally decreased participants’ self-reported willingness and ability to drive a motor vehicle, and increased levels of intoxication and BAC estimations relative to placebo. With regard to group differences, DUI offenders showed an increased sensitivity to the disrupting effects of alcohol on impulsive choices, such that DUI offenders showed a significantly greater preference for impulsive choices under alcohol relative to placebo than controls. Taken together, these findings provide some of the first pieces of evidence that compared to controls, DUI offenders display an increased tendency for impulsive decisions under alcohol, which likely contributes to risky decisions to drive after drinking, despite clear evidence for their behavioral impairment. These findings could have important implications for understanding the mechanisms underlying maladaptive behaviors in this high-risk population, and sheds light on possible targets for intervention to reduce DUI recidivism