Autonomous vehicles (AVs) or self-driving cars have the potential to provide many benefits such as improving mobility and reducing the energy and emissions consumed, travel time, and vehicle ownership. Thus, in the last few years, both research and industry have put significant efforts to develop AVs. However, laws and regulations are not ready yet for this switch and the legal sector is unable to take the lead but follow the development of AVs. Besides, the social acceptance is considered as a main key factor for the success of any new technology. Despite the enthusiastic speculation of AVs, little is known about the public acceptance and perception of the AVs technology or the factors that influence the public acceptance. This paper reviews the previous studies that focuses on testing the public acceptance and perception of AVs and sketches out the main trends in this area to provide some directions and recommendations for the future. This paper focuses on the influence of safety, ethics, liability, regulations, and the recent pandemic on the public acceptance of AVs.

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Public acceptance and perception of autonomous vehicles: a comprehensive review.

Conditionally automated driving (CAD) relieves the driver from monitoring current traffic conditions. This type of driving inherently enables the driver to execute different non-driving-related tasks (NDRTs). However, the driver still must be available as a backup option. With this in mind, the classification and evaluation of various NDRTs concerning their impact on driver performance in takeover scenarios represent an important contribution toward the creation of safe CAD functions. We reviewed various NDRTs that were used in studies on automated driving. The focus was on assigning aspects of these activities (e.g., ability to visually monitor traffic, necessity of sustained attention to NDRT, etc.) to various steps of the takeover process (e.g., noticing and interpreting takeover requests), which could be impaired by the execution of the respective NDRT. This, in turn, would increase the demands on the driver with respect to managing the takeover situation.

A Review of Non-driving-related Tasks Used in Studies on Automated Driving

Driver assistance systems, also called automated driving systems, allow drivers to immerse themselves in non-driving-related tasks. Unfortunately, drivers may not trust the automated driving system, which prevents either handing over the driving task or fully focusing on the secondary task. We assert that enhancing situational awareness can increase trust in automation. Situational awareness should increase trust and lead to better secondary task performance. This study manipulated situational awareness by providing them with different types of information: the control condition provided no information to the driver, the low condition provided a status update, while the high condition provided a status update and a suggested course of action. Data collected included measures of trust, trusting behavior, and task performance through surveys, eye-tracking, and heart rate data. Results show that situational awareness both promoted and moderated the impact of trust in the automated vehicle, leading to better secondary task performance. This result was evident in measures of self-reported trust and trusting behavior.

Situational Awareness, Drivers Trust in Automated Driving Systems and Secondary Task Performance

The last decade witnessed a great development of automated driving vehicles (ADVs) and vehicle intelligence. The significant increment of machine intelligence poses a new challenge to the community, which is the collaboration between human drivers and vehicle autonomy. As vehicle autonomy is gaining more control authority, the roles that human drivers can play in the future need to be further clarified. In this study, literature review and perspectives on the human behaviors and cognition (HBC) for ADVs toward human-autonomy (H-A) collaboration are proposed. First, the H-A collaboration basics and key factors are reviewed. Then, the HBC issues in driver behavior modeling and understanding are discussed. Specifically, two key factors are reviewed, which are human trust and situation awareness (SA). Next, HBC in two H-A collaboration-enabled vehicle control methods, namely, shared control and take-over control, are analyzed. It is shown that the human-centered collaboration strategy that integrates driver states will largely increase the acceptance of the ADVs. Then, the HBC issues in the design of human-machine-interface (HMI) for future autonomous and collaboration-enabled ADVs are discussed. Last, challenges and future works for H-A collaboration on ADVs are analyzed to contribute to the development of understandable, trustable, and acceptable ADVs.

Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving

Designing safe and effective systems for control transitions between human and vehicle is a difficult task, due to increased reaction times and potentially inattentive drivers In order to respond to these difficulties, this paper presents an overview of interaction solutions for control transitions between manual and autonomous driving modes The paper examines technology patents, as well as academic publications The paper's first contribution is an examination of the current state of the art of control transition interfaces in automated vehicles The paper's second contribution is the reusable categorization framework developed for this overview The results are used to identify holes and potentials regarding control transition design, including strong focus on the system over the human, lacking fallback performance, and the potentials of effective driving mode communication These aspects point the way towards the challenges to be solved -- together with how they might be solved -- for safe and effective control transitions

Control Transition Interfaces in Semiautonomous Vehicles: A Categorization Framework and Literature Analysis

Driving in an Increasingly Automated World – Approaches to Improve the Driver-automation Interaction

Highly automated driving is on the advance and is linked to various benefits such as increased overall comfort for the driver as well as rising fuel and transport efficiency. Especially within the domain of truck driving in terms of long distance haulage, it seems as if highly automated driving systems could positively enhance the conditions for drivers and transport companies. Literature suggests that the acceptance of new technologies is a major determinant of whether a developing technology is used. This paper describes an approach aimed at assessing truck drivers’ attitudes towards highly automated driving. Furthermore, fleet managers’ opinions regarding the potential and limitations of highly automated driving systems were queried to investigate whether transport companies would invest in this new technology. Data was collected by an online and paper-based questionnaire. The results reflect the major areas of acceptance and doubts towards highly automated driving from the drivers’ as well as the fleet managers’ perspectives. Both groups are found to be the most concerned about legal liability issues and the general safety and reliability of such technology. Comfort and safety seem to have the biggest influence on the acceptance of highly automated driving. Truck drivers were concerned about reduced driving pleasure as well as being redundant. Results show that the majority of truck drivers do not have a clear idea of highly automated systems. In contrast, fleet managers claim to have an idea of the system.

Assessing Truck Drivers’ and Fleet Managers’ Opinions Towards Highly Automated Driving

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

https://www.mdpi.com/2414-4088/2/4/68/pdf

Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

Vehicle automation is linked to various benefits such as an increase in fuel and transport efficiency, as well as an increase in driving comfort. Automation also comes with a variety of downsides e.g. loss of situation awareness, loss of skills as well as inappropriate trust levels regarding system functionality. Drawbacks differ between automation levels. As highly-automated driving (level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human-machine interface (HMI) arises. To foster adequate and efficient humanmachine interaction, this contribution presents a user-centered, iterative approach for HMI design for highly-automated truck driving.An expert workshop was conducted to develop first ideas and HMI ketches. Workshop results were combined with scientific findings regarding HMI design for highly-automated car driving. Based on those findings, a paper prototype was created and evaluated with experts, using an approach of mixed qualitative methods (heuristic evaluation, thinking aloud). The outcome was implemented to the HMI concept. In a third step, the HMI was conceptualized as video prototype enabling a more detailed evaluation. Again, experts were asked to assess the HMI using qualitative (thinking aloud) and quantitative methods (questionnaires).The result represents a video prototype showing a HMI strategy for highly-automated driving, aiming at fostering a successful human-machine interaction. Relevant issues such as drivers’ informational needs, situation awareness and trust were explicitly considered during HMI design. Next steps comprise HMI implementation and user evaluation in a driving simulator to let users experience the HMI in a semi-real driving context.

Conceptual design and evaluation of a human machine interface for highly automated truck driving

The general role of personal assistants in form of anthropomorphised conversational, virtual or robotic agents in cars is subject to research since a few years and the first results indicate numerous positive effects of these anthropomorphised interfaces. However, no comprehensive review of the conducted studies has been comprised yet. Furthermore, existing studies on the effect of anthropomorphism mainly focus on passenger cars. This article provides a comprehensive review and summary of the conducted studies and investigates the applicability to commercial transportation, in particular to anthropomorphised interaction between truck driver and truck. In the first part of the article, a literature review describes the details, aspects and various forms of anthropomorphism as well as its observed positives effects. The review focusses on studies referring to anthropomorphism in passenger cars, complemented by relevant research results from non-automotive disciplines. The second part of this article aims to derive innovative and applicable concepts for the anthropomorphised driver-truck interfaces using the Design-Thinking approach: building on a comprehensive literature review to identify user needs and problems, an interdisciplinary expert workshop developed the two first anthropomorphised driver-truck interaction concepts. The paper finishes with carving out the differences between anthropomorphised car-driver and truck-driver interaction. The next step of research will then be the implementation of the developed interaction concepts in a first prototype followed by the respective user evaluation.

Natalie Tara Richardson

Papers

Driving in an Increasingly Automated World – Approaches to Improve the Driver-automation Interaction

Assessing Truck Drivers’ and Fleet Managers’ Opinions Towards Highly Automated Driving

Takeover Requests in Highly Automated Truck Driving: How Do the Amount and Type of Additional Information Influence the Driver–Automation Interaction?

Conceptual design and evaluation of a human machine interface for highly automated truck driving

Anthropomorphising driver-truck interaction: a study on the current state of research and the introduction of two innovative concepts