How will vehicle automation and electrification affect the automotive maintenance, repair sector?

Automated driving offers great possibilities in traffic safety advancement. However, evidence of safety cannot be provided by current validation methods. One promising solution to overcome the approval trap (Winner, 2015) could be the scenario-based approach. Unfortunately, this approach still results in a huge number of test cases. One possible way out is to show the current, incorrect path in the argumentation and strategy of vehicle automation, and focus on the systemic mechanisms of road traffic safety. This paper therefore argues the case for defining relevant scenarios and analysing them systemically in order to ultimately reduce the test cases. The relevant scenarios are based on the strengths and weaknesses, in terms of the driving task, for both the human driver and automation. Finally, scenarios as criteria for exclusion are being proposed in order to systemically assess the contribution of the human driver and automation to road safety.

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The Way Forward for Indirect Structural Health Monitoring (iSHM) Using Connected and Automated Vehicles in Europe

Infrastructures ◽

10.3390/infrastructures6030043 ◽

2021 ◽

Vol 6 (3) ◽

pp. 43

Author(s):

Konstantinos Gkoumas ◽

Kyriaki Gkoktsi ◽

Flavio Bono ◽

Maria Cristina Galassi ◽

Daniel Tirelli

Keyword(s):

Intelligent Transportation Systems ◽

Intelligent Transportation ◽

Transportation Systems ◽

Transport Infrastructure ◽

Policy Framework ◽

Strategic Decisions ◽

Automated Vehicles ◽

Civil Infrastructures ◽

Vehicle Automation ◽

Labor Requirements

Europe’s aging transportation infrastructure requires optimized maintenance programs. However, data and monitoring systems may not be readily available to support strategic decisions or they may require costly installations in terms of time and labor requirements. In recent years, the possibility of monitoring bridges by indirectly sensing relevant parameters from traveling vehicles has emerged—an approach that would allow for the elimination of the costly installation of sensors and monitoring campaigns. The advantages of cooperative, connected, and automated mobility (CCAM), which is expected to become a reality in Europe towards the end of this decade, should therefore be considered for the future development of iSHM strategies. A critical review of methods and strategies for CCAM, including Intelligent Transportation Systems, is a prerequisite for moving towards the goal of identifying the synergies between CCAM and civil infrastructures, in line with future developments in vehicle automation. This study presents the policy framework of CCAM in Europe and discusses the policy enablers and bottlenecks of using CCAM in the drive-by monitoring of transport infrastructure. It also highlights the current direction of research within the iSHM paradigm towards the identification of technologies and methods that could benefit from the use of connected and automated vehicles (CAVs).

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Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

Information ◽

10.3390/info12010013 ◽

2020 ◽

Vol 12 (1) ◽

pp. 13

Author(s):

Thierry Bellet ◽

Aurélie Banet ◽

Marie Petiot ◽

Bertrand Richard ◽

Joshua Quick

Keyword(s):

Artificial Intelligence ◽

Adaptive Management ◽

Driving Simulator ◽

End Users ◽

Point Of View ◽

Human Centered Design ◽

Perceived Utility ◽

New Type ◽

Future Potential ◽

Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.

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OESDs in an on-road study of semi-automated vehicle to human driver handovers

Cognition Technology & Work ◽

10.1007/s10111-021-00682-z ◽

2021 ◽

Author(s):

Neville A. Stanton ◽

James W. Brown ◽

Kirsten M. A. Revell ◽

Jisun Kim ◽

Joy Richardson ◽

...

Keyword(s):

Vehicle Control ◽

Future Research ◽

Strong Correlations ◽

Sequence Diagrams ◽

Driving Simulators ◽

Human Driver ◽

Handover Process ◽

Vehicle Automation ◽

The Uk ◽

Road Study

AbstractDesign of appropriate interaction and human–machine interfaces for the handover of control between vehicle automation and human driver is critical to the success of automated vehicles. Problems in this interfacing between the vehicle and driver have led, in some cases, to collisions and fatalities. In this project, Operator Event Sequence Diagrams (OESDs) were used to design the handover activities to and from vehicle automation. Previous work undertaken in driving simulators has shown that the OESDs can be used to anticipate the likely activities of drivers during the handover of vehicle control. Three such studies showed that there was a strong correlation between the activities drivers represented in OESDs and those observed from videos of drivers in the handover process, in driving simulators. For the current study, OESDs were constructed during the design of the interaction and interfaces for the handover of control to and from vehicle automation. Videos of drivers during the handover were taken on motorways in the UK and compared with the predictions from the OESDs. As before, there were strong correlations between those activities anticipated in the OESDs and those observed during the handover of vehicle control from automation to the human driver. This means that OESDs can be used with some confidence as part of the vehicle automation design process, although validity generalisation remains an important goal for future research.

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Opportunities and Challenges in Cooperative Road Vehicle Automation

IEEE Open Journal of Intelligent Transportation Systems ◽

10.1109/ojits.2021.3099976 ◽

2021 ◽

pp. 1-1

Author(s):

Steven E. Shladover

Keyword(s):

Road Vehicle ◽

Vehicle Automation

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Supporting User Onboarding in Automated Vehicles through Multimodal Augmented Reality Tutorials

Multimodal Technologies and Interaction ◽

10.3390/mti5050022 ◽

2021 ◽

Vol 5 (5) ◽

pp. 22

Author(s):

Henrik Detjen ◽

Robert Niklas Degenhart ◽

Stefan Schneegass ◽

Stefan Geisler

Keyword(s):

Augmented Reality ◽

Task Performance ◽

Online Survey ◽

Smartphone Application ◽

Assistant Systems ◽

Vehicle Automation ◽

Hedonic Quality ◽

First Time ◽

Reducing Potential ◽

And Task

Misconceptions of vehicle automation functionalities lead to either non-use or dangerous misuse of assistant systems, harming the users’ experience by reducing potential comfort or compromise safety. Thus, users must understand how and when to use an assistant system. In a preliminary online survey, we examined the use, trust, and the perceived understanding of modern vehicle assistant systems. Despite remaining incomprehensibility (36–64%), experienced misunderstandings (up to 9%), and the need for training (around 30%), users reported high trust in the systems. In the following study with first-time users, we examine the effect of different User Onboarding approaches for an automated parking assistant system in a Tesla and compare the traditional text-based manual with a multimodal augmented reality (AR) smartphone application in means of user acceptance, UX, trust, understanding, and task performance. While the User Onboarding experience for both approaches shows high pragmatic quality, the hedonic quality was perceived significantly higher in AR. For the automated parking process, reported hedonic and pragmatic user experience, trust, automation understanding, and acceptance do not differ, yet the observed task performance was higher in the AR condition. Overall, AR might help motivate proper User Onboarding and better communicate how to operate the system for inexperienced users.

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Impact of External Human–Machine Interface Communication Strategies of Automated Vehicles on Pedestrians’ Crossing Decisions and Behaviors in an Urban Environment

Sustainability ◽

10.3390/su13158396 ◽

2021 ◽

Vol 13 (15) ◽

pp. 8396

Author(s):

Marc Wilbrink ◽

Merle Lau ◽

Johannes Illgner ◽

Anna Schieben ◽

Michael Oehl

Keyword(s):

Urban Traffic ◽

Automated Vehicles ◽

Negative Effects ◽

Communication Tools ◽

Automated Vehicle ◽

Additional Information ◽

Road Users ◽

Vulnerable Road Users ◽

And Behaviors ◽

Vehicle Automation

The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results.

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