Unsettled Issues in Remote Operation for On-road Driving Automation

2021 ◽  
Author(s):  
Sven Beiker ◽  
Keyword(s):  
Public Space ◽  
Cyber Security ◽  
Situational Awareness ◽  
Automated Driving ◽  
Driving System ◽  
Additional Layer ◽  
Human Aspects ◽  
Remote Operation ◽  
Entire Vehicle ◽  
Potential Risks

On-road vehicles equipped with driving automation features—where a human might not be needed for operation on-board—are entering the mainstream public space. However, questions like “How safe is safe enough?” and “What to do if the system fails?” persist. This is where remote operation comes in, which is an additional layer to the automated driving system where a human remotely assists the so-called “driverless” vehicle in certain situations. Such remote-operation solutions introduce additional challenges and potential risks as the entire vehicle-network-human now needs to work together safely, effectively, and practically. Unsettled Issues in Remote Operation for On-road Driving Automation highlights technical questions (e.g., network latency, bandwidth, cyber security) and human aspects (e.g., workload, attentiveness, situational awareness) of remote operation and introduces evolving solutions. The report also discusses standards development and regulations—both of which are needed to provide frameworks for the deployment of driving automation with remote operation.

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

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

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.

When the automated driving system fails: Dynamics of public responses to automated vehicles

2021 ◽  
Vol 129 ◽  
pp. 103271
Author(s):  
Zhigang Xu ◽  
Zijun Jiang ◽  
Guanqun Wang ◽  
Runmin Wang ◽  
Tingting Li ◽  
...  
Keyword(s):  
Automated Vehicles ◽  
Automated Driving ◽  
Driving System

In Pursuit of Emergency Procedures for Automated Driving System-Involved Scenarios

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Travis Terry ◽  
Tammy E. Trimble ◽  
Mindy Buchanan-King ◽  
Myra Blanco ◽  
Vikki L. Fitchett ◽  
...  
Keyword(s):  
Automated Driving ◽  
Driving System ◽  
Emergency Procedures

scholarly journals Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Frederik Naujoks ◽  
Yannick Forster ◽  
Katharina Wiedemann ◽  
Alexandra Neukum
Keyword(s):  
Driving Simulator ◽  
Primary Task ◽  
Automated Driving ◽  
Task Interference ◽  
Driving System ◽  
Lane Changes ◽  
Speech Output ◽  
Driving Time ◽  
Standard Information ◽  
Machine Interface

During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver’s primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visual-auditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

Applications of Machine Learning in Cyber Security

2020 ◽  
pp. 83-103 ◽  
Author(s):  
Charu Virmani ◽  
Tanu Choudhary ◽  
Anuradha Pillai ◽  
Manisha Rani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Technological Systems ◽  
The World ◽  
Computer Scientists ◽  
Potential Risks ◽  
To Come ◽  
Time Basis ◽  
Applications Of Machine Learning

With the exponential rise in technological awareness in the recent decades, technology has taken over our lives for good, but with the application of computer-aided technological systems in various domains of our day-to-day lives, the potential risks and threats have also come to the fore, aiming at the various security features that include confidentiality, integrity, authentication, authorization, and so on. Computer scientists the world over have tried to come up, time and again, with solutions to these impending problems. With time, attackers have played out complicated attacks on systems that are hard to comprehend and even harder to mitigate. The very fact that a huge amount of data is processed each second in organizations gave birth to the concept of Big Data, thereby making the systems more adept and intelligent in dealing with unprecedented attacks on a real-time basis. This chapter presents a study about applications of machine learning algorithms in cyber security.

Exploring Cyber Security Vulnerabilities in the Age of IoT

2018 ◽  
pp. 1609-1623 ◽  
Author(s):  
Shruti Kohli
Keyword(s):  
Cyber Security ◽  
Low Cost ◽  
Cyber Attacks ◽  
Rolling Stock ◽  
Security Vulnerabilities ◽  
Security Breaches ◽  
Closed Networks ◽  
Train Control ◽  
Potential Risks ◽  
Physical Assets

The modernization of rail control systems has resulted in an increasing reliance on digital technology and increased the potential for security breaches and cyber-attacks. Higher-level European Train Control System(ETCS) systems in particular depend on communications technologies to enable greater automation of railway operations, and this has made the protecting the integrity of infrastructure, rolling stock, staff and passengers against cyber-attacks ever more crucial. The growth in Internet of Things (IoT) technology has also increased the potential risks in this area, bringing with it the potential for huge numbers of low-cost sensing devices from smaller manufacturers to be installed and used dynamically in large infrastructure systems; systems that previously relied on closed networks and known asset identifiers for protection against cyber-attacks. This chapter demonstrates that how existing data resources that are readily available to the railways could be rapidly combined and mapped to physical assets. This work contributes for developing secure reusable scalable framework for enhancing cyber security of rail assets

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