Unsettled Issues Regarding Communication of Automated Vehicles with Other Road Users

Mapping Intimacies ◽

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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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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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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Ethics of Artificial Intelligence in Transport

The Oxford Handbook of Ethics of AI ◽

10.1093/oxfordhb/9780190067397.013.42 ◽

2020 ◽

pp. 667-683

Author(s):

Bryant Walker Smith

Keyword(s):

Artificial Intelligence ◽

Ethical Issues ◽

Ethical Question ◽

Power Dynamics ◽

Automated Vehicles ◽

Automated Driving ◽

The U.S ◽

Policy Solutions

This chapter highlights key ethical issues in the use of artificial intelligence in transport by using automated driving as an example. These issues include the tension between technological solutions and policy solutions; the consequences of safety expectations; the complex choice between human authority and computer authority; and power dynamics among individuals, governments, and companies. In 2017 and 2018, the U.S. Congress considered automated driving legislation that was generally supported by many of the larger automated-driving developers. However, this automated-driving legislation failed to pass because of a lack of trust in technologies and institutions. Trustworthiness is much more of an ethical question. Automated vehicles will not be driven by individuals or even by computers; they will be driven by companies acting through their human and machine agents. An essential issue for this field—and for artificial intelligence generally—is how the companies that develop and deploy these technologies should earn people’s trust.

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Automated vehicles and the morality of post-collision behavior

Ethics and Information Technology ◽

10.1007/s10676-021-09607-w ◽

2021 ◽

Author(s):

Sebastian Krügel ◽

Matthias Uhl ◽

Bryn Balcombe

Keyword(s):

European Commission ◽

Research Program ◽

Collision Detection ◽

Expert Group ◽

Automated Vehicles ◽

Automated Driving ◽

Data Provision

AbstractWe address the considerations of the European Commission Expert Group on the ethics of connected and automated vehicles regarding data provision in the event of collisions. While human drivers’ appropriate post-collision behavior is clearly defined, regulations for automated driving do not provide for collision detection. We agree it is important to systematically incorporate citizens’ intuitions into the discourse on the ethics of automated vehicles. Therefore, we investigate whether people expect automated vehicles to behave like humans after an accident, even if this behavior does not directly affect the consequences of the accident. We find that appropriate post-collision behavior substantially influences people’s evaluation of the underlying crash scenario. Moreover, people clearly think that automated vehicles can and should record the accident, stop at the site, and call the police. They are even willing to pay for technological features that enable post-collision behavior. Our study might begin a research program on post-collision behavior, enriching the empirically informed study of automated driving ethics that so far exclusively focuses on pre-collision behavior.

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First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

it - Information Technology ◽

10.1515/itit-2020-0023 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jonas Andersson ◽

Azra Habibovic ◽

Daban Rizgary

Keyword(s):

Driver Behavior ◽

Added Value ◽

Future Research ◽

Gaze Behavior ◽

Repeated Testing ◽

Automated Vehicles ◽

Automated Driving ◽

Perceived Safety ◽

Repeated Interaction ◽

Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

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The Design of Preventive Automated Driving Systems Based on Convolutional Neural Network

Electronics ◽

10.3390/electronics10141737 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1737

Author(s):

Wooseop Lee ◽

Min-Hee Kang ◽

Jaein Song ◽

Keeyeon Hwang

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Processing Speed ◽

Model Comparison ◽

Distance Estimation ◽

Visual Object ◽

Suitable Model ◽

Automated Vehicles ◽

Automated Driving

As automated vehicles have been considered one of the important trends in intelligent transportation systems, various research is being conducted to enhance their safety. In particular, the importance of technologies for the design of preventive automated driving systems, such as detection of surrounding objects and estimation of distance between vehicles. Object detection is mainly performed through cameras and LiDAR, but due to the cost and limits of LiDAR’s recognition distance, the need to improve Camera recognition technique, which is relatively convenient for commercialization, is increasing. This study learned convolutional neural network (CNN)-based faster regions with CNN (Faster R-CNN) and You Only Look Once (YOLO) V2 to improve the recognition techniques of vehicle-mounted monocular cameras for the design of preventive automated driving systems, recognizing surrounding vehicles in black box highway driving videos and estimating distances from surrounding vehicles through more suitable models for automated driving systems. Moreover, we learned the PASCAL visual object classes (VOC) dataset for model comparison. Faster R-CNN showed similar accuracy, with a mean average precision (mAP) of 76.4 to YOLO with a mAP of 78.6, but with a Frame Per Second (FPS) of 5, showing slower processing speed than YOLO V2 with an FPS of 40, and a Faster R-CNN, which we had difficulty detecting. As a result, YOLO V2, which shows better performance in accuracy and processing speed, was determined to be a more suitable model for automated driving systems, further progressing in estimating the distance between vehicles. For distance estimation, we conducted coordinate value conversion through camera calibration and perspective transform, set the threshold to 0.7, and performed object detection and distance estimation, showing more than 80% accuracy for near-distance vehicles. Through this study, it is believed that it will be able to help prevent accidents in automated vehicles, and it is expected that additional research will provide various accident prevention alternatives such as calculating and securing appropriate safety distances, depending on the vehicle types.

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Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994580 ◽

2021 ◽

pp. 036119812199458

Author(s):

Slobodan Gutesa ◽

Joyoung Lee ◽

Dejan Besenski

Keyword(s):

Travel Time ◽

Information Exchange ◽

Fixed Time ◽

Optimization Method ◽

Time Signal ◽

Market Penetration ◽

Automated Vehicles ◽

Automated Driving ◽

Concept Evaluation ◽

Intersection Management

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

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Driver intervention performance assessment as a key aspect of L3–L4 automated vehicles deployment

Open Engineering ◽

10.1515/eng-2021-0079 ◽

2021 ◽

Vol 11 (1) ◽

pp. 845-852

Author(s):

Aleksandra Rodak ◽

Paweł Budziszewski ◽

Małgorzata Pędzierska ◽

Mikołaj Kruszewski

Keyword(s):

Performance Assessment ◽

Minimal Risk ◽

Automated Vehicles ◽

Automated Driving ◽

Driving System ◽

Driving Task ◽

Driving Conditions

Abstract In L3–L4 vehicles, driving task is performed primarily by automated driving system (ADS). Automation mode permits to engage in non-driving-related tasks; however, it necessitates continuous vigilance and attention. Although the driver may be distracted, a request to intervene may suddenly occur, requiring immediate and appropriate response to driving conditions. To increase safety, automated vehicles should be equipped with a Driver Intervention Performance Assessment module (DIPA), ensuring that the driver is able to take the control of the vehicle and maintain it safely. Otherwise, ADS should regain control from the driver and perform a minimal risk manoeuvre. The paper explains the essence of DIPA, indicates possible measures, and describes a concept of DIPA framework being developed in the project.

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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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