Human Factors in Automotive Engineering and Technology

Guy H. Walker, Neville A. Stanton, GUY H WALKER

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9781138747258
• eBook ISBN: 9781317120278
• Edition: 1
• Topic: Technik & Maschinenbau
• Subtopic: Transportwesen

Offering a unique perspective on vehicle design and on new developments in vehicle technology, this book seeks to bridge the gap between engineers, who design and build cars, and human factors, as a body of knowledge with considerable value in this domain. The work that forms the basis of the book represents more than 40 years of experience by the authors. Human Factors in Automotive Engineering and Technology imparts the authors' scientific background in human factors by way of actionable design guidance, combined with a set of case studies highly relevant to current technological challenges in vehicle design. The book presents a novel and accessible insight into a body of knowledge that will enable students, professionals and engineers to add significant value to their work.

Chapter 1
The Car of the Future, Here Today¹

Every field has its luminaries. These are people who produce that ‘key’ text or ‘definitive work’, people who propose ideas and concepts that lead one to ask: ‘Why didn’t I think of that?’ In human factors research, one such person is Professor Donald Norman. Many in the field of Vehicle Design will be familiar with this name. It is associated with the famous book The Design of Everyday Things (1990), along with more recent publications dealing with the Problems of Automation (1990), The Invisible Computer (1999), Emotional Design (2003), The Design of Future Things (2007) and Living with Complexity (2010). We have had the pleasure of exchanging ideas with him. The discussion began with a request for copies of our papers on vehicle automation, which we sent, and a stimulating conversation ensued. So, rather than offering this as a ‘normal’ book introduction, we thought we could present the conversation, interspersed with the relevant sections from the papers, to help orientate you to what the issues are and what we intend to cover in this book.

Not a Normal Introduction

It all started in 1995 with the front portion of a Ford Orion (see Figure 1.1). The rear potion, sadly, did not fit through the narrow doorway in Southampton University’s Murray Building through which it needed to be squeezed. Nor did the roof, which had to be cut off and reattached. Our first driving simulator used the partially reassembled remains of the Ford Orion, a first-generation Epson LCD projector, an Archimedes RISC computer and an enthusiastic computer programmer who built the simulation software from scratch, was able to diagnose faults by looking at the raw machine code and made all the other vehicles in the simulation look like Rover 200s. As a facility it was crude but surprisingly effective. Remember, in 1995 driving simulators, as we know them today, were not common.

From these humble beginnings, the lab went considerably up-market with a pre-production prototype Jaguar XK8 sports car, this time housed in a garage with a door big enough to avoid having to cut it in half. In 1999 it moved to a dedicated driving lab at Brunel University in London, where it was joined by a Ford Mondeo. This vehicle was donated by Ford themselves, an ex-test vehicle with strange ‘emergency’ buttons fitted around the cabin and non-standard modifications to the brakes. The company who supplied cinema screens to Odeon also provided our screens, and for a while a modified version of a driving game was used. This enabled the real-life Ford Mondeo simulator vehicle to become a virtual Dodge Viper, and this was sometimes required for serious-minded ‘test purposes’.

Figure 1.1 The driving simulator laboratory has been through several iterations in its 20-year history: This is the first, dating from 1995 and based around the front portion of a Ford Orion

Figure 1.2 The Brunel University Driving Simulator (BUDS) in 2000

Figure 1.3 The current iteration (2013): The Southampton University Driving Simulator (SUDS)

Today the lab is back at the University of Southampton with a Jaguar XJ as its centrepiece. It has 135 degree wrap-around screens and the latest vehicle telematics and actuation – a far cry from the front portion of a Ford Orion and an equally vintage Archimedes A4000 RISC computer.

We have been at work in this laboratory, and out on the road, for nearly 20 years now – or between us a combined period of 50 years or more – and most of it has been directed at understanding the effects of vehicle automation on driver performance. This is how our conversation with Don Norman started.

So, Why Automate?

Don Norman: ‘All the people in the auto companies that I talk with defend the use of automation because it will “relax” the driver’ (Stanton, Young and Walker, 2007, p. 289).

Us: ‘We often hear the same thing. The arguments favouring automation of the driver role seem to take at least three forms. The first assumes driving is an extremely stressful activity and, the suggestion goes, automating certain driving activities could help make significant improvements to the driver’s well-being. The second argument is similar. Given the fact human error constitutes a major cause of road accidents, it could be reasonably suggested that the removal of the human element from the control loop may ultimately lead to a reduction in accident statistics. The final argument is based on economic considerations and presumes automation will enhance the desirability of the product and thus lead to substantial increases in unit sales. We examine this in more detail in Chapters 2 and 3 but for now we can dwell on the fact that, whether we like it or not, automation is gradually taking over the driver’s role’ (ibid., p. 289).

Extract from Stanton and Young (2000, pp. 315–16)

Full vehicle automation is predicted to be on British roads by 2030. Whilst it is accepted that some drivers will still want to control their vehicles manually, many may be pleased to relinquish the role to automatic systems. Many of the computing technologies have been grounded in aviation systems (Stanton and Marsden, 1996), and technologies like Adaptive Cruise Control (ACC) are taking over from the driver already. ACC heralds a new generation of vehicle (Stanton et al., 1997). ACC controls both speed and headway of the vehicle, braking with limited authority in the presence of a slower lead vehicle, and an ability to return to the set speed when the lead vehicle disappears. In this way ACC differs from traditional Cruise Control (CC) systems. In traditional CC, the system relieves the driver of foot control of the accelerator only (i.e., relieving the driver of some physical workload), whereas ACC relieves the driver of some of the decision making elements of the task, such as deciding to brake or change lanes (i.e., relieving the driver of some mental workload). Potentially, then, ACC is a welcome additional vehicle system that will add comfort and convenience to the driver. However, certain psychological issues arise when considering any form of automation and these need to be properly addressed to improve overall system performance. It is envisaged that although the ACC system will behave in exactly the manner prescribed by the designers and programmers, there may still be scenarios in which the driver’s perception of the situation is at odds with the system’s operation (Stanton and Young, 1998). Indeed, even those developing the systems recognise that ‘headway control raises the issue of whether the system matches driver expectations with regard to braking and headway control’.

Problems and Ironies

Don Norman: ‘The following incident was told to me recently by a friend. What do you make of it? Driving on the highway with ACC. Lots of traffic, so the vehicle is travelling slowly. The car now reaches its exit point, so the driver turns off the highway on to the exit lane. But the driver had forgotten that he was in ACC mode. The ACC, noting the absence of vehicles in front, rapidly accelerated to highway speeds, which is quite dangerous on the exit lane. The driver braked in time, slowing the car and disengaging ACC. This is a classic example of mode error. What do you think?’ (Stanton, Young and Walker, 2007, p. 294).

Us: ‘It strikes us that these incidents (including that of your friend) are rather like the mode errors seen in other transport domains. For example, the two state warning device fitted into train cabs that alerts drivers to upcoming events (like signals or speed restrictions); the driver “losing track” of what the warning refers to has been cited in several major accidents and incidents. Likewise, in the aviation sector, there are numerous instances of the autopilot being inadvertently and unknowingly configured for one course of action when another was desired. This idea of “losing track” is an interesting one. To use your phrase, vehicles already provide the kind of “informal chatter” in the form of feedback that helps keep drivers attentive and in-the-loop, and we look more closely at this in Chapter 8’.

Extract from Walker, Stanton and Young (2006, pp. 162–4)

Situational awareness (SA) is about ‘knowing what is going on’ (Endsley, 1995). A key component of driving is knowing about the vehicle’s current position in relation to its destination, the relative positions and behaviour of other vehicles and hazards, and also knowing how these critical variables are likely to change in the near future (Gugerty, 1997; Sukthankar, 1997). Moment to moment knowledge of this sort enables effective decisions to be made in real time and for the driver to be ‘tightly coupled to the dynamics of [their] environment’ (Moray, 2004). Why is this important? It is because poor SA is a greater cause of accidents than improper speed or driving technique (Gugerty, 1997). The irony is that modern trends in automotive design appear to be diminishing the level and type of vehicle feedback available to the driver.

The vehicle is an intermediate variable between the driver’s control inputs and the environment within which those inputs are converted into outputs (of changes in trajectory and/or velocity). In converting driver inputs to vehicle outputs the vehicle sustains various stresses, the results of which can be perceived by the driver as they interact with the controls. A lot of this feedback is non-visual. In the case of auditory feedback, this comprises principally of engine, transmission, tyre and aerodynamic noise (Wu et al., 2003). Drivers have been shown to make relatively little use of overt visual aids such as the speedometer (e.g., Mourant and Rockwell, 1972), using implicit auditory cues instead. Diminishing auditory feedback also leads to several unexpected behavioural consequences. Horswill and McKenna report that ‘drivers who received the quieter internal car noise … chose to drive faster than those who received louder car noises’ (1999, p. x). Not only that, but quieter cars tend to encourage reduced headway and more risky gap acceptance (Horswill and Coster, 2002).

Also consider the more complex example of tactile feedback in the form of ‘steering feel’. Steering feel arises because the control inceptor (the steering wheel) is mechanically linked to the system (the arrangement of vehicle suspension and tyres) that is undergoing the stress of converting driver inputs into desired changes in trajectory. The stresses arise partly from disturbances involving the road surface, from stored energy in the vehicle’s tyres and from a characteristic referred to as aligning torque (Jacobson, 1974). Aligning torque is an expression of the effort required by the driver to hold the steering wheel in its desired position. Within the normal envelope of vehicle dynamics, the more aligning torque present, the more cornering force is developed by the vehicle’s tyres (e.g., it takes more effort to hold the wheel stationary when cornering at 70 mph than it does at 20 mph) (Jacobson, 1974; Becker et al., 1995). In a seminal paper, Joy and Hartley describe aligning torque as giving the driver ‘a measure of the force required to steer the car, i.e. it gives a measure of the “feel” at the steering wheel’ (1953–4, p. x). It is interesting to consider that beyond a very low torque threshold, many power steering systems (as are now fitted to the majority of modern cars) significantly diminish, or at least change the effect of aligning torque, thus altering the feedback on vehicle state that might otherwise be conveyed to the driver (Jacobson, 1974). Steering feel and auditory feedback, and the host of other instances where input from the environment might otherwise be emitted from or through the vehicle, occurs as a byproduct of the car being an ostensibly mechanical device; the vehicle itself does not require it. There are a number of instances within automotive design and engineering where non-visual feedback cues like these are effectively being ‘designed-out’. This situation is not passing entirely unnoticed, as one motor industry commentator opines in the context of the ‘art of safe driving’:

One of the problems with modern cars is that they have been developed in such a way as to insulate all the occupants from the outside world as far as possible … almost always at the expense of the driver knowing what is going on (Loasby, 1995, p. 2).

The situation has certain parallels with trends in aviation, but unlike the attention devoted to these other areas (e.g., Field and Harris, 1998), automotive systems seem to have gone largely unexamined (MacGregor and Slovic, 1989). Of course, the examples described above would be of little concern if drivers were insensitive to these aspects of vehicle design. The evidence, however, points to the reverse situation. Hoffman and Joubert (1968) obtained just noticeable difference data on a number of vehicle-handling variables and they discovered ‘a very high differential sensitivity to changes of [vehicle] response time, and reasonably good ability to detect changes of steering ratio and stability factor’. Joy and Hartley (1953–4) describe this level of sensitivity as corresponding roughly to ‘the difference in feel of a medium-size saloon car with and without a fairly heavy passenger in the rear seat’. In a study about vehicle vibration, Mansfield and Griffin (2000) report a similarly high level of sensitivity, as do a range of further studies (e.g. Segel, 1964; Horswill and Coster, 2002). This presents a further irony, because the very small changes required for normal drivers to detect differences in vehicle feedback, and thus for it to potentially affect their SA, stand in contrast to some of the very large changes proposed in automotive engineering, such as drive-by-wire (Walker et al., 2001). Drive-by-wire is the automotive equivalent of trends in aviation whereby the control inceptor is ‘electronically’ connected to the system under control as opposed to ‘mechanically’ connected. For example, in most modern cars ‘the accelerator pedal is simply an input to the engine management computer … The driver command may be overridden or modified [by the engine management system] in pursuit of other vehicle objectives’ (Ward and Woodgate, 2004). The same design philosophy is to be applied to vehicle brakes and even steering (see Chapter 2). Clearly, such changes are of a magnitude potentially far greater than the difference in feel between having a fairly large passenger in the rear seat or not (Joy and Hartley, 1953–4). And that is before we even get to the ‘normal’ human factors domain of advanced driver automation systems.

Don Norman: ‘Hmm. Your analysis is that it is not wise to relax the driver’ (Stanton, Young and Walker, 2007, p. 300).

Us: ‘I suppose we would prefer an attentive driver rather than a relaxed one. Either way, we are still worried the driver could find him or herself fighting with these systems, which would be reminiscent of the “problem” with automation described in your own paper. Indeed, this is the topic of Chapter 9, where we look at a driver’s ability to regain control with adaptive cruise control, and Chapter 11, where we look at how automated systems such as these interact with trust in technology’ (ibid., p. 300).

Well-intentioned Technologies

There can be no doubt that vehicle automation is a well-intentioned technology with the ‘potential’ to increase driver safety, efficiency and enjoyment. The important ‘contingency factor’ that sits between ‘potential’ and ‘act...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Contents
• List of Figures
• List of Tables
• About the Authors
• Acknowledgements
• Glossary
• 1 The Car of the Future, Here Today
• 2 A Technology Timeline
• 3 Lessons from Aviation
• 4 Defining Driving
• 5 Describing Driver Error
• 6 Examining Driver Error and its Causes
• 7 A Psychological Model of Driving
• 8 Vehicle Feedback and Driver Situational Awareness
• 9 Vehicle Automation and Driver Workload
• 10 Automation Displays
• 11 Trust in Vehicle Technology
• 12 A Systems View of Vehicle Automation
• 13 Conclusions
• Appendix
• Further Reading
• References
• Bibliography
• Index