Automobile Automation

Distributed Cognition on the Road

Victoria A. Banks, Neville A. Stanton

• Publisher: CRC Press
• Year: 2017
• ISBN:
  9781315295633
• Edition: 1
• Topic: Computer Science
• Subtopic: Human-Computer Interaction
• Index: Computer Science

Increasing levels of driving automation has changed the role of the driver from active operator to passive monitor. However, Systems Design has been plagued by criticism for failing to acknowledge the new role of the driver within the system network. To understand the driver's new role within an automated driving system, the theory of Distributed Cognition is adopted. This approach provides a useful framework for the investigation of allocation of function between multiple agents in the driving system. A Systems Design Framework has been developed that outlines how the Distributed Cognition paradigm can be applied to driving using both qualitative and quantitative research methodologies.

1

Introduction to Automobile Automation

Introduction

The Defence Advanced Research Project Agency (DARPA) has fuelled interest into the field of ‘vehicle automation’ since the 1950s as it was recognised that automated vehicles could be used to gather intelligence, be used in surveillance operations and for target acquisition and reconnaissance (Rouff and Hinchey, 2012). The agency placed emphasis on maintaining technological superiority and security as well as reducing the number of personnel required on the ground.

DARPA is most famously recognised for its Grand Challenges (2004, 2005) and Urban Challenge (2007) that invited teams to build and design fully autonomous vehicles. The first Grand Challenge in 2004 aimed to show that autonomous vehicles could undertake resupply missions in unfamiliar desert terrain. Although no vehicles completed the course, success was finally achieved in 2005, demonstrating that unmanned vehicles could navigate across remote environments, on a variety of road surfaces with different obstacles and with limited or no global positioning satellites (Rouff and Hinchey, 2012). The 2007 Urban Challenge was designed to test the ability of autonomous vehicles to navigate safely and effectively through populated areas to simulate supply missions while adhering to normal driving laws. At this point, safety was of utmost importance and all vehicles had to be equipped with a form of ‘E-Stop’ – autonomous emergency braking – to maintain the safety of DARPA employees and spectators. It is these advancements that fuelled research and innovation within the automotive industry as the capabilities of automated vehicles to improve the safety of the roads and its occupants had been recognised.

In line with the advancements facilitated by DARPA, the introduction of automated driving features into ‘civilian’ life has gradually risen since 2000. The main purpose of automated driving features from a marketing point of view is to continue the trend of safe, comfortable, efficient and enjoyable personal travel as well as bring about improvements to traffic efficiency and fuel consumption (e.g. Khan et al., 2012; Ward, 2000). In the 1990s, cruise assist technologies increased in popularity, and as autonomy appeared to improve the driving experience, more advanced features were developed.

While fully autonomous vehicles (i.e. vehicles requiring no human operator) were developed for the DARPA Challenges and more recently by Google in 2014, automation within the automotive industry requires an acknowledgement of Human Factors in the design of automated driving features because the driver remains an active participant within the driving task to some extent. Although over recent years, technological advancements have meant that vehicles have become increasingly capable of performing the same functions as the driver to a much greater degree, there continues to be a stipulation within the law that drivers must remain in overall control of their vehicle (e.g. Article 8 of the Vienna Convention, 1968). A recent amendment to the convention in 2014 (introduced in 2016) states that driverless cars are allowed on the road as long as they can be overridden by a human driver. This means now more than ever, the driver needs to remain capable of regaining control of an automated vehicle and be supported to do so following prolonged exposure to periods of highly automated driving where boredom and fatigue may become increasingly problematic.

The research presented in this book offers one of the first acknowledgements of how the introduction of automation into the driving task fundamentally changes the role of the driver within it using task analysis modelling techniques. The aim therefore is not to deliver specific data or guidelines about ‘how’ to manage a transfer of control in autonomy but instead identify and increase our understanding of the changing role of the driver within the totality of the driving system.

Safety research suggests that driver inattentiveness and lack of timely response to unpredictable or incomplete information are the most common driver errors that result in vehicular accidents (Amditis et al., 2010; Cantin et al., 2009; Donmez et al., 2007; Khan et al., 2012; Stanton and Salmon, 2009). These external factors are typically random events that evolve to form complex interactions between the driver and the vehicle (Khan et al., 2012). Without automated assistance, the driver may be underprepared or lack the skill required to respond to the situation accordingly. For this reason, automated vehicles have great potential to improve the safety of our roads and in turn reduce the economic burden of any cumulative effect as a result of an accident such as sick pay through injury and impact to businesses if roads are closed. To put this into focus, the World Health Organisation (WHO) has stated that if current road traffic accident trends continue, the annual fatalities as a consequence of such accidents will increase to 2.34 million by 2020 (Khan et al., 2012). In 2012, the WHO declared that approximately 1.3 million people per annum die as a result of road traffic accidents. Nearly half of these (46%) are considered to be ‘vulnerable road users’. Deaths resulting from road traffic accidents are the leading cause of injury mortality, offering a clear justification for investing time into the field of vehicle automation. If the benefits of automation outweigh potential costs, then automation may prove to be beneficial in economic, societal and environmental terms (Khan et al., 2012; Stanton and Marsden, 1996; Young et al., 2011). However, despite the expectation that automation will bring about enhancement of road safety, such hypotheses require further validation (Stanton and Marsden, 1996). Further research is needed to assess the degree to which automation can reduce the overall number of driver errors that are often implicated as the cause of many vehicular accidents.

Since 1997, the European New Car Assessment Programme (Euro NCAP) has continued to encourage vehicle manufacturers to exceed the minimal safety requirements that are required by law. It also aims to ensure that stringent guidelines and testing protocols are rigorously enforced to ensure that potential new customers are given transparent safety information through use of its internationally recognised Five-Star Rating Scheme. By rewarding technologies, Euro NCAP pushes vehicle manufactures to accelerate their standard fitment of key automated safety technologies such as Blind Spot Monitoring, Lane Support Systems, Speed Alert Systems, Autonomous Emergency Braking, Automatic Emergency Call and Pre-Crash Systems.

There are of course other reasons why automation may be beneficial. For example, automated driving may not only improve road safety, but also reduce traffic congestion, exhaust gas emissions and fuel consumption according to the European Commission (2011). Interest in automated driving as a form of ‘Traffic Management System’ continues to grow as demonstrated through the 9th Intelligent Transport Systems European Congress (2013), which included a special interest session that looked specifically at the future of highly automated vehicles (including highway trucks and vehicle platooning) as well as automated urban transportation. Although air quality has been an environmental concern for some time, transport is currently a major source of air pollution within the United Kingdom, and with car use set to increase further, more needs to be done to tackle the problem of congestion and its associated impacts both economically and environmentally (Fagnant and Kockelman, 2015). There are a number of approaches that can be used to improve air quality, and new vehicle technologies can play an important role in addressing these environmental issues further.

With systems design plagued by criticism for failing to adequately define the role of the human operator within a system, there is concern among the Ergonomics and Human Factors community that automated subsystems in driving may create more problems than they solve. Failing to acknowledge the role of the driver in an automated driving system therefore may lead to undesirable behavioural adaptation as a result of inadequately anticipating the changing role of the driver within the system. This is likely to become even more problematic as multiple vehicle subsystems, operating at different levels of automation, are interacting. It is also a very important area of study given recent legislation that requires the driver to be capable of regaining control of an automated vehicle.

This research attempts to address concerns surrounding driver behavioural adaptation in three main ways:

1.Increase the awareness of Human Factors in the design of automated aids by focussing on the interaction that occurs between the driver and other system agents

With growing concern that the role of the driver is not being fully recognised in the design of automated driving systems, it is important to focus upon the interaction that occurs between the driver and system agents at differing levels of autonomy. This allows for exploration of the diminishing role of the driver with regard to direct vehicle control and what this may mean to the overall functioning of this sociotechnical system (Walker et al., 2010). Importantly, this book is primarily concerned with understanding the role of the driver at intermediate levels of automation. Thus, it is not a book about ‘driverless’ vehicles whereby human operators are free to do whatever they want. The authors argue that no matter how small their role, the role of the driver remains an important design consideration.

2.Assess the appropriateness of automation deployment and context of use

Human Factors would argue that even though it may be possible to fully automate a vehicle, it may not always be appropriate to do so given the limits of human attention needed to execute a required response. An automatic braking system, for example, could relinquish driver control over a critical safety function. This may be appropriate to do so in scenarios whereby the driver has not got the capacity to respond, such as 500 ms prior to a collision. Such an autonomous feature, however, may cause drivers to become more reliant on its presence. This may result in increased reaction times and stopping distances as drivers ‘wait’ for the system to engage.

3.Provide design guidance on automated features based upon experimental evidence

Being able to provide systems design guidance to vehicle manufacturers is extremely important to ensure that the functionality of driver–vehicle interaction is optimised as far as is reasonably practicable.

Outline of Book

Chapter 1: Introduction to This Book

This initial chapter introduces the area of driving automation and outlines the aims and objectives of the research. It also includes a summary of each chapter and indicates the contribution to knowledge.

Chapter 2: On the Road to Full Vehicle Automation

This chapter introduces the concept of automation and the different levels at which it can be introduced into a system, thus altering the role of the human operator within it. Multiple automation taxonomies are discussed that have sought to better define ‘who’ is doing ‘what’ at varying levels of automation. What all automation taxonomies have in common is that at higher levels, the level of control that the human operator has over a system is reduced. However, this does not mean that they become completely removed from the system altogether. Instead, they remain to some extent within the control-feedback loop. This is because they continue to receive feedback from the automated system and their wider environment. In terms of driving, the driver will continue to receive feedback from the automated system via the Human–Machine Interface (HMI) within the vehicle in addition to feedback from the wider road environment even when the vehicle is capable of performing much of the driving task autonomously. This means that driver responsibilities continue to change as the level of automation increases. Assessing whether drivers are able to adhere to these changing responsibilities requires an acknowledgement of key Human Factors considerations. Chapter 2 reviews the literature relating to four key Human Factors concepts: situation awareness, driver workload, trust and skill and concludes that automation can have both positive and negative effects on each of these dimensions.

Chapter 3: Adopting a Systems Engineering View

Previous research into automation has traditionally been either Technology-Focussed or Human-Centred. However, this chapter adopts an increasingly popular sociotechnica...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgements
• Authors
• List of Abbreviations
• List of Figures
• List of Tables
• Chapter 1: Introduction to Automobile Automation
• Chapter 2: On the Road to Full Vehicle Automation
• Chapter 3: Adopting a Systems View in the Design of Automated Driving Features
• Chapter 4: Exploring the Use of Verbal Protocol Analysis as a Tool to Analyse Driver Behaviour
• Chapter 5: Using Retrospective Verbal Protocols to Explore Driver Behaviour in Emergencies
• Chapter 6: The Effect of Systems Design on Driver Behaviour: The Case of AEB
• Chapter 7: What Is Next for Vehicle Automation? From Design Concept through to Prototype Development
• Chapter 8: Discovering Driver–Vehicle Coordination Problems in Early-Stage System Development
• Chapter 9: Driver-Initiated Design: An Approach to Keeping the Driver in Control?
• Chapter 10: Distributed Cognition in the Road Transportation Network: A Comparison of ‘Current’ and ‘Future’ Networks
• Chapter 11: Summary of Findings and Research Approach
• References
• Index