Addressing inequal risk exposure in the development of automated vehicles

Ethics and Information Technology ◽

10.1007/s10676-021-09610-1 ◽

2021 ◽

Author(s):

Manuel Dietrich

Keyword(s):

Risk Taking ◽

Social Inequalities ◽

Road Traffic ◽

Risk Estimation ◽

Risk Exposure ◽

Automated Vehicles ◽

Automated Driving ◽

Moderate Risk ◽

Road Users ◽

Different Types

AbstractAutomated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities.

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Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

Journal of Advanced Transportation ◽

10.1155/2021/8859553 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jaehyun Jason So ◽

Sungho Park ◽

Jonghwa Kim ◽

Jejin Park ◽

Ilsoo Yun

Keyword(s):

Driving Simulator ◽

Road Traffic ◽

Vehicle Control ◽

Automated Vehicles ◽

Automated Driving ◽

Automated Vehicle ◽

The Road ◽

Traffic Conditions ◽

Vehicle System ◽

Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

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Analysis of the advancements in real-life performance of highly automated vehicles' with regard to the road traffic safety

MATEC Web of Conferences ◽

10.1051/matecconf/201823105003 ◽

2018 ◽

Vol 231 ◽

pp. 05003 ◽

Cited By ~ 1

Author(s):

Arkadiusz Matysiak ◽

Paula Razin

Keyword(s):

Traffic Safety ◽

Large Scale ◽

Road Traffic ◽

Real Life ◽

Automated Vehicles ◽

Automated Driving ◽

The Road ◽

Road Traffic Safety ◽

On The Road ◽

The Impact

The article presents the analysis of the performance of the vehicles equipped with automated driving systems (ADS) which were tested in real-life road conditions from 2015 to 2017 in the state of California. It aims at the effort to assess the impact on the road safety the continuous technological advancements in driving automation might have, based on of the first large-scale, real-life test deployments. Vehicle manufacturers and other stakeholders testing the highly automated vehicles in California are obliged to issue yearly reports which provide an insight on the test scale as well as the technology maturity. The so-called 'disengagement reports' highlight the range and number of control takeovers between the ADS and driver, which are made either based on driver's decision or information provided by the vehicle itself. The analysis of these reports allowed to investigate the development made in automated driving technology throughout the years of tests, as well as the direct or indirect influence of the external factors (e.g. various weather conditions) on the ADS performance. The results show that there is still a significant gap in reliability and safety between human drivers and highly automated vehicles which has been yet steadily decreasing due to technology advancements made while driving in the specific infrastructure and traffic conditions of California.

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Trust in Automation as a Matter of Media Influence and Experi-ence of Automated Vehicles

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601460 ◽

2016 ◽

Vol 60 (1) ◽

pp. 2024-2028 ◽

Cited By ~ 6

Author(s):

Anna Feldhütter ◽

Christian Gold ◽

Adrian Hüger ◽

Klaus Bengler

Keyword(s):

Road Safety ◽

Driving Simulator ◽

Intention To Use ◽

Automated Vehicles ◽

Automated Driving ◽

Trust In Automation ◽

Different Types ◽

The Media ◽

Source Of Information ◽

Highly Automated Driving

Highly automated vehicles (HAV), which could help to enhance road safety and efficiency, are very likely to enter the market within the next decades. To have an impact, these systems need to be purchased, which is a matter of trust and acceptance. These factors are dependent on the level of information that one has about such systems. One important source of information is various media, such as newspapers, magazines and videos, in which highly automated driving (HAD) is currently a frequent topic of discussion. To evaluate the influence of media on the perception of HAD, 31 participants were presented with three different types of media addressing HAD in a neutral manner. Afterwards, the participants experienced HAD in the driving simulator. In between these steps, the participants completed questionnaires assessing comfort, trust in automation, increase in safety, intention to use and other factors in order to analyze the effect of the media and the driving simulation experience. Results indicate that the perception of some aspects of HAD were affected by the media presented, while experiencing HAD in the driving simulator generally did not have an effect on the attitude of the participants. Other aspects, such as trust, were not affected by either media or experience. In addition, gender-related differences in the perception of HAD were found.

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Unsettled Issues Regarding Communication of Automated Vehicles with Other Road Users

10.4271/epr2020023 ◽

2020 ◽

Author(s):

Sven Beiker ◽

Keyword(s):

Traditional Approach ◽

Research Report ◽

Automated Vehicles ◽

Automated Driving ◽

Road Users ◽

Planning Organizations ◽

Telecommunication Companies ◽

Major Shift ◽

Implicit And Explicit ◽

Multiple Scenarios

The focus of this SAE EDGE™ Research Report is to address a topic overlooked by many who choose to view automated driving systems and AVs from a “10,000-foot” perspective: how automated vehicles (AVs) will actually communicate with other road users. Conventional (human-driven) vehicles, bicyclists, and pedestrians already have a functioning system of understating each other while on the move. Adding automated vehicles to the mix requires assessing the spectrum of existing modes of communication – both implicit and explicit, biological and technological, and how they will interact with each other in the real world. The impending deployment of AVs represents a major shift in the traditional approach to ground transportation; its effects will inevitably be felt by parties directly involved with the vehicle manufacturing and use and those that play roles in the mobility ecosystem (e.g., aftermarket and maintenance industries, infrastructure and planning organizations, automotive insurance providers, marketers, telecommunication companies). Unsettled Issues Regarding Communication of Automated Vehicles with Other Road Users brings together the multiple scenarios we are likely to see in a future not too far away and how they are likely to play out in practical ways.

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Augmented Reality for Future Mobility: Insights from a Literature Review and HCI Workshop

i-com ◽

10.1515/icom-2021-0029 ◽

2021 ◽

Vol 20 (3) ◽

pp. 295-318

Author(s):

Andreas Riegler ◽

Andreas Riener ◽

Clemens Holzmann

Keyword(s):

Augmented Reality ◽

Literature Review ◽

Road Safety ◽

Status Quo ◽

Literature Analysis ◽

Enabling Technology ◽

Automated Vehicles ◽

Automated Driving ◽

The Past ◽

Road Users

Abstract There is a growing body of research in the field of interaction between drivers/passengers and automated vehicles using augmented reality (AR) technology. Furthering the advancements and availability of AR, the number of use cases in and around vehicles rises. Our literature review reveals that in the past, AR research focussed on increasing road safety and displaying navigational aids, however, more recent research explores the support of immersive (non-)driving related activities, and finally enhance driving and passenger experiences, as well as assist other road users through external human-machine interfaces (HMIs). AR may also be the enabling technology to increase trust and acceptance in automated vehicles through explainable artificial intelligence (AI), and therefore help on the shift from manual to automated driving. We organized a workshop addressing AR in automotive human-computer interaction (HCI) design, and identified a number of challenges including human factors issues that need to be tackled, as well as opportunities and practical usages of AR in future mobility. We believe that our status-quo literature analysis and future-oriented workshop results can serve as a research agenda for user interface designers and researchers when developing automotive AR interfaces.

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From risk perception to accident

Sigurnost ◽

10.31306/s.63.2.1 ◽

2021 ◽

Vol 63 (2) ◽

pp. 125-142

Author(s):

Seth Oppong

Keyword(s):

Decision Making ◽

Risk Perception ◽

Path Analysis ◽

Risk Taking ◽

Traffic Accidents ◽

Safety Climate ◽

Commercial Vehicle ◽

Road Traffic ◽

Risk Exposure ◽

Road Traffic Accidents

The purpose of this study is to investigate the influence of comprehension of road hazards communication designs and safety climate on risk perception as well as the effect of the latter on road traffic accidents among commercial vehicle drivers in Ghana. Two hundred and twenty-six (226) commercial vehicle drivers participated in this study. Questionnaires were administered to drivers who travel outbound from Accra to nine (9) other regions of Ghana to enhance the external validity of the research findings. Path analysis, using structural equation modelling, was performed on the data obtained. Results of the SEM or path analysis revealed that all the hypothesized relationships were significant except three paths. The non-significant ones included the paths from RHCDs comprehension to risk perception and to driver decision making respectively, as well as the path from driver decision making to risk-taking behaviour. Overall, the model fitting showed that the proposed model for the study derived principally from the risk chain process model has empirical support. The implications are that risk perception influences risk-taking behaviour and decision making, whereas the latter influences risk exposure. In addition, safety climate influences risk perception, risk-taking behaviour, and road traffic accidents. Similarly, risk-taking behaviour influences risk exposure while risk exposure influences road traffic involvement. These implications were discussed in the light of the existing theory and extant empirical literature.

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Risk estimation for driving support and behavior planning in intelligent vehicles

at - Automatisierungstechnik ◽

10.1515/auto-2017-0132 ◽

2018 ◽

Vol 66 (2) ◽

pp. 119-131 ◽

Cited By ~ 2

Author(s):

Julian Eggert

Keyword(s):

Risk Estimation ◽

Intelligent Vehicles ◽

Risk Modeling ◽

Foreseeable Future ◽

Automated Driving ◽

Safe Driving ◽

Different Types ◽

Driving Task ◽

And Behavior ◽

Highly Automated Driving

AbstractVehicles will be equipped with sensors and functions for highly automated driving in the foreseeable future. A big topic of research on the way to this goal is how to convey to these vehicles an understanding of the driving situations that is comparable to that of humans. For safe driving, this requires predicting how a scene will evolve and anticipating how dangerous it will potentially be. Risk estimation is a central ingredient in this process. In this paper, we describe how risk modeling frameworks help in managing the complexity of the driving task. We approach risk from the perspective of rare probabilistic events in environments where predictions might be inherently uncertain, and explain how this leads to a survival-based formulation which allows to model different types of risks encountered in driving situations within a single unified concept. In addition, we show how the framework can be used for driving behavior evaluation and risk-avoiding trajectory planning.

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From HMI to HMIs: Towards an HMI Framework for Automated Driving

Information ◽

10.3390/info11020061 ◽

2020 ◽

Vol 11 (2) ◽

pp. 61 ◽

Cited By ~ 16

Author(s):

Klaus Bengler ◽

Michael Rettenmaier ◽

Nicole Fritz ◽

Alexander Feierle

Keyword(s):

Research And Development ◽

Influencing Factors ◽

Automated Driving ◽

Research Gaps ◽

Automated Vehicle ◽

Road Users ◽

Human Machine Interfaces ◽

Factors Influencing ◽

Different Types ◽

The Relationship

During automated driving, there is a need for interaction between the automated vehicle (AV) and the passengers inside the vehicle and between the AV and the surrounding road users outside of the car. For this purpose, different types of human machine interfaces (HMIs) are implemented. This paper introduces an HMI framework and describes the different HMI types and the factors influencing their selection and content. The relationship between these HMI types and their influencing factors is also presented in the framework. Moreover, the interrelations of the HMI types are analyzed. Furthermore, we describe how the framework can be used in academia and industry to coordinate research and development activities. With the help of the HMI framework, we identify research gaps in the field of HMI for automated driving to be explored in the future.

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Can Automated Vehicles Improve Cyclist Safety in Urban Areas?

Safety ◽

10.3390/safety5030057 ◽

2019 ◽

Vol 5 (3) ◽

pp. 57 ◽

Cited By ~ 1

Author(s):

Pavlos Tafidis ◽

Ali Pirdavani ◽

Tom Brijs ◽

Haneen Farah

Keyword(s):

Urban Areas ◽

Road Traffic ◽

Flow Simulation ◽

Simulation Software ◽

Assessment Model ◽

Automated Vehicles ◽

Passenger Cars ◽

The Road ◽

Road Users ◽

Traffic Flow Simulation

Automated vehicles (AVs) are expected to assist in decreasing road traffic fatalities, particularly among passenger cars. However, until now limited research has been conducted on how they will impact the safety of vulnerable road users (VRUs) (i.e., cyclists and pedestrians). Therefore, there is a clear need to start taking into account the interactions between AVs and VRUs as an integrated element of the transport network, especially in urban areas where they are dominant. The objective of this study is to verify whether the anticipated implementation of AVs can actually improve cyclists’ safety. For this purpose, the microscopic traffic flow simulation software PTV Vissim combined with the surrogate safety assessment model (SSAM) were utilized. The road network used for this analysis was generated based on a real study case in a medium-sized city in Belgium, where narrow streets in the city center are shared on many occasions between vehicles and cyclists. The findings of the analysis show a notable reduction in the total number of conflicts between cars, but also between cars and cyclists, compared to the current situation, assuming a 100% market penetration scenario for AVs. Moreover, the severity level of conflicts also decreased as a result of the lack of human-driven vehicles in the traffic streams.

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