745 research outputs found
Too sick to drive : how motion sickness severity impacts human performance
There are multiple concerns surrounding the development and rollout of self-driving cars. One issue has largely gone unnoticed - the adverse effects of motion sickness as induced by self-driving cars. The literature suggests conditionally, highly and fully autonomous vehicles will increase the onset likelihood and severity of motion sickness. Previous research has shown motion sickness can have a significant negative impact on human performance. This paper uses a simulator study design with 51 participants to assess if the scale of motion sickness is a predictor of human performance degradation. This paper finds little proof that subjective motion sickness severity is an effective indicator of the scale of human performance degradation. The performance change of participants with lower subjective motion sickness is mostly statistically indistinguishable from those with higher subjective sickness. Conclusively, those with even acute motion sickness may be just as affected as those with higher sickness, considering human performance. Building on these results, it could indicate motion sickness should be a consideration for understanding user ability to regain control of a self-driving vehicle, even if not feeling subjectively unwell. Effectiveness of subjective scoring is discussed and future research is proposed to help ensure the successful rollout of self-driving vehicles
User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle
Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.
Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority
Objective
A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control).
Background
During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance.
Method
Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions.
Results
Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road.
Conclusion
Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing driversâ authority was detrimental for both performance and comfort.
Application
As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important
Prediction of driversâ performance in highly automated vehicles
Purpose: The aim of this research was to assess the predictability of driverâs response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect driversâ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driverâs physiological data. Statistical and machine learning methods were used to assess the predictability of driversâ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the driversâ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driverâs secondary tasks engagement and correlated with the driverâs subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driverâs response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the driversâ ability to respond to future critical hazards. More research is required to find the influence of age, driversâ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting driversâ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driverâs physiological state to allow for the safest transition possible. In addition, it provides an insight into driverâs readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div
User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle
Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the
vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date,
little is known about driver expectations of partial driving automation and whether this affects the information
they require inside the vehicle. Twenty-five participants were presented with five partially automated driving
events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data
was coded and analysed using grounded theory. From the results, two groupings of driver expectations were
identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two
groups the information preferences differed. LIP drivers did not want detailed information about the vehicle
presented to them, but the definition of partial automation means that this kind of information is required for
safe use. Hence, the results suggest careful thought as to how information is presented to them is required in
order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the systemâs status and driving and were found to be more willing to work with the partial
automation and its current limitations. It was evident that the driversâ expectations of the partial automation
capability differed, and this affected their information preferences. Hence this study suggests that HMI designers
must account for these differing expectations and preferences to create a safe, usable system that works for
everyone
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
Driving Towards Inclusion: Revisiting In-Vehicle Interaction in Autonomous Vehicles
This paper presents a comprehensive literature review of the current state of
in-vehicle human-computer interaction (HCI) in the context of self-driving
vehicles, with a specific focus on inclusion and accessibility. This study's
aim is to examine the user-centered design principles for inclusive HCI in
self-driving vehicles, evaluate existing HCI systems, and identify emerging
technologies that have the potential to enhance the passenger experience. The
paper begins by providing an overview of the current state of self-driving
vehicle technology, followed by an examination of the importance of HCI in this
context. Next, the paper reviews the existing literature on inclusive HCI
design principles and evaluates the effectiveness of current HCI systems in
self-driving vehicles. The paper also identifies emerging technologies that
have the potential to enhance the passenger experience, such as voice-activated
interfaces, haptic feedback systems, and augmented reality displays. Finally,
the paper proposes an end-to-end design framework for the development of an
inclusive in-vehicle experience, which takes into consideration the needs of
all passengers, including those with disabilities, or other accessibility
requirements. This literature review highlights the importance of user-centered
design principles in the development of HCI systems for self-driving vehicles
and emphasizes the need for inclusive design to ensure that all passengers can
safely and comfortably use these vehicles. The proposed end-to-end design
framework provides a practical approach to achieving this goal and can serve as
a valuable resource for designers, researchers, and policymakers in this field
Use and citation of paper "Fox et al (2018), âWhen should the chicken cross the road? Game theory for autonomous vehicle - human interactions conference paperâ" by the Law Commission to review and potentially change the law of the UK on autonomous vehicles. Cited in their consultation report, "Automated Vehicles: A joint preliminary consultation paper" on p174, ref 651.
Topic of this consultation: The Centre for Connected and Automated Vehicles (CCAV) has
asked the Law Commission of England and Wales and the Scottish Law Commission to
examine options for regulating automated road vehicles. It is a three-year project, running from
March 2018 to March 2021. This preliminary consultation paper focuses on the safety of
passenger vehicles.
Driving automation refers to a broad range of vehicle technologies. Examples range from
widely-used technologies that assist human drivers (such as cruise control) to vehicles that
drive themselves with no human intervention. We concentrate on automated driving systems
which do not need human drivers for at least part of the journey.
This paper looks at are three key themes. First, we consider how safety can be assured before
and after automated driving systems are deployed. Secondly, we explore criminal and civil
liability. Finally, we examine the need to adapt road rules for artificial intelligence
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