Is in-Vehicle Background Audio Distracting to Drivers?

Driving an automobile is one of the most automatized, but also complex tasks completed on a daily basis. Beyond merely operating pedals and a steering wheel, drivers need to maintain situational awareness and respond to unexpected events by other drivers, as well as other visual and auditory signals. Drivers often engage in secondary, non-driving tasks while driving as well, such as listening to music. Some drivers routinely drive in the presence of very high levels of background audio. Previous studies have offered differing conclusions on whether in-vehicle background audio can affect driving behavior, possibly by increasing the driver’s cognitive workload. In the present study, nineteen adult participants performed a variety of tasks in the presence of different levels and types of background audio, while operating a simulated vehicle at The Ohio State Driving Simulation Laboratory. Drivers also performed two secondary tasks to assess cognitive workload while driving: performing complicated arithmetic calculations on numbers on billboards placed in unexpected locations in the scenario, and rating the urgency of visual and auditory warning signals presented at varied intervals in the scenario. Measures of driving performance included speed, following behavior, steering smoothness, and lane keeping capabilities. Results showed that increased audio levels decrease the perceived urgency of warnings, and increase the number of errors in the arithmetic task. Driving performance was not impacted, suggesting that high audio levels reduce overall situational awareness and increase cognitive workload.No embargoAcademic Major: Speech and Hearing Scienc

Automated driving: A literature review of the take over request in conditional automation

This article belongs to the Special Issue Autonomous Vehicles TechnologyIn conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver's state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.This work was funded by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0; the Spanish Ministry of Economy, Industry and Competitiveness under the TRA201563708-R and TRA2016-78886-C3-1-R project; open access funding by the Johannes Kepler University Linz

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

Designing Auditory Warning Signals to Improve the Safety of Commercial Vehicles

Based on four studies, this thesis aims to explore how to design auditory warning signals that can facilitate safer driving by operators of heavy goods vehicles. The first three studies focus on the relationships between certain characteristics of auditory warnings and various indicators of traffic safety. A deeper understanding of these relationships would allow system developers to design auditory signals that are better optimised for safety. The fourth study examines the opinions of both vehicle developers and professional drivers regarding warning attributes. One major conclusion is that meaningful warning sounds that are related to the critical event can improve safety. As compared with arbitrarily mapped sounds, meaningful sounds are easier to learn, can improve drivers’ situation awareness, and generate less interference and less annoyance. The present thesis also supports the view that commercial drivers’ initial acceptance of these sounds may be very high. Annoyance is an especially important aspect of warning design to consider; it can negatively influence driving performance and may lead drivers to turn off their warning systems. This research supports the notion that drivers do not consider that negative experience is an appropriate attribute of auditory warnings designed to increase their situation awareness. Also, commercial drivers seem to report, significantly more than vehicle developers, that having less-annoying auditory warnings is important in high-urgency driving situations. Furthermore, the studies presented in this thesis indicate that annoyance cannot be predicted based on the physical properties of the warning alone. Learned meaning, appropriateness of the mapping between a warning and a critical event, and individual differences between drivers may also significantly influence levels of annoyance. Arousal has been identified as an important component of driver reactions to auditory warnings. However, high levels of arousal can lead to a narrowing of attention, which would be suboptimal for critical situations during which drivers need to focus on several ongoing traffic events. The present work supports the notion that high-urgency warnings can influence commercial drivers’ responses to unexpected peripheral events (i.e., those that are unrelated to the warning) in terms of response force, but not necessarily in terms of response time. The types of auditory warnings that will be developed for future vehicles depend not only on advances in research, but also on the opinions of developers and drivers. The present research shows that both vehicle developers and drivers are aware of several of the potentially important characteristics of auditory warnings. For example, they both recognise that warnings should be easy to understand. However, they do disagree regarding certain attributes of warnings, and, furthermore, developers may tend to employ a “better safe than sorry” strategy (by neglecting factors concerning annoyance and the elicitation of severe startled responses) when designing high-urgency warnings. Developers’ recognition of the potentially important attributes of auditory warnings should positively influence the future development of in-vehicle systems. However, considering the current state of research regarding in-vehicle warnings, it remains challenging to predict the most suitable sounds for specific warning functions. One recommendation is to develop a design process that examines the appropriateness of in-vehicle auditory warnings. This thesis suggests an initial version of such a process, which in this case was produced in collaboration with system designers working in the automotive industry

DECISION-MAKING PROCESSES, DRIVING PERFORMANCE, AND ACUTE RESPONSES TO ALCOHOL IN DUI OFFENDERS

Alcohol-impaired driving is a major cause of motor vehicle accident and death in the United States. People who are arrested for DUI (Driving under the Influence) are at high risk to reoffend; approximately one in three of these individuals will commit another DUI offense in the three years following their first conviction (Nochajski & Stasiewicz, 2006). This high risk for recidivism in these individuals suggests that cognitive characteristics may contribute to a pattern of pathological decision making leading to impaired driving. Indeed, individuals with a history of DUI report higher rates of impulsiveness and behavioral dysregulation compared to their nonoffending peers. Relatively little research, however, has used laboratory methods to identify the specific behavioral characteristics, such as poor inhibitory control or heightened sensitivity to immediate reward, which may differentiate DUI offenders from nonoffenders. Further, little is known about how individuals with a history of DUI respond following an acute dose of alcohol. Study 1 examined impulsivity in 20 adults with a recent DUI conviction and 20 adults with no history of DUI using self-report and behavioral measures of impulsivity. This study also used a novel decision-making paradigm to examine how different levels of risk and reward influenced the decision to drive after drinking in both groups. Results of this study found that DUI offenders did not differ from controls in their performance on behavioral measures of impulsivity. They did, however, report higher levels of impulsivity and demonstrated a greater willingness to tolerate higher levels of risk for more modest rewards. Study 2 examined the acute effects of alcohol and expectancy manipulation on driving performance and decision making in the same group of participants. Neither alcohol nor expectancy manipulation exerted a systematic effect on decision making in either group. Alcohol impaired driving performance equally in both groups, but the DUI group perceived themselves as less impaired by alcohol. Expectancy manipulation eliminated this group difference in perceived driving ability. Taken together, these findings identify processes that risk of impaired driving in DUI offenders. They may perceive themselves as less impaired by alcohol, leading to risky decision making when drinking. Expectancy manipulation may be a viable method of reducing risky decision making in DUI offenders

Integrating Multiple Alarms & Driver Situation Awareness

This study addresses this gap in CAS and intelligent alarm research by examining whether or not a single master alarm warning versus multiple warnings for the different collision warning systems conveys adequate information to the drivers. Intelligent driver warning systems signaling impending frontal and rear collisions, as well as unintentional lane departures were used in this experiment, and all the warnings were presented to drivers through the auditory channel only. We investigated two critical research questions in this study: 1. Do multiple intelligent alarms as opposed to a single master alarm affect drivers’ recognition, performance, and action when they experience a likely imminent collision and unintentional lane departure? 2. Is driver performance and overall situation awareness under the two different alarm alerting schemes affected by reliabilities of the warning systems?Prepared For Ford Motor Compan

Predicting Driver Takeover Performance in Conditionally Automated Driving

http://deepblue.lib.umich.edu/bitstream/2027.42/156409/1/AAP_Predicting_takeover_performance.pdfSEL

Takeover performance evaluation using driving simulation: a systematic review and meta-analysis

Introduction: In a context of increasing automation of road transport, many researchers have been dedicated to analyse the risks and safety implications of resuming the manual control of a vehicle after a period of automated driving. This paper performs a systematic review about drivers' performance during takeover manoeuvres in driving simulator, a tool that is widely used in the evaluation of automated systems to reproduce risky situations that would not be possible to test in real roads. Objectives: The main objectives are to provide a framework for the main strategies, experimental conditions and results obtained by takeover research using driving simulation, as well as to find whether different approaches may lead to different outcomes. Methodology: First, a literature search following the PRISMA statement guidelines and checklist resulted in 36 relevant papers, which were described in detail according to the type of scenarios and takeover events, drivers' engagement in secondary tasks and the assessed takeover performance measures. Then, those papers were included in a meta-analysis combining PAM clustering and ANOVA techniques to find patterns among the experimental conditions and to determine if those patterns have influence on the observed takeover performance. Conclusions: Less complex experiments without secondary task engagement and conducted in low-fidelity simulators are associated with lower takeover times and crash rates. The takeover time increases with the time budget of the first alert, which reduces the pressure for a driver's quick intervention. (c) 2021, The Author(s)