Driver information and assistance systems have emerged as an integral part of modern road vehicles in order to support the driver while driving. They make use of the newest information technologies in order to enhance driver awareness, safety and comfort, and thereby avoiding driver errors and accidents. Driver Adaptation to Information and Assistance Systems brings together recent work by the Marie-Curie Initial Training Network ADAPTATION. The project has studied drivers' behavioural adaptation to these new technologies from an integrative perspective working under a joint conceptual theoretical framework of behavioural adaptation that can be used to generate research hypotheses about how drivers will adapt to information and assistance systems and to derive guidelines for the design and deployment of such systems. The book aims to provide the reader with a better understanding of drivers' adaptation processes over time in response to information and assistance system use at different levels (energetic, cognitive and motivational levels); an appreciation of the impact of specificities of drivers population on technology use and skill acquisition; insights on the effects of system functionality, design and reliability as important system characteristics influencing drivers' adaptation; and recommendations on research methods and appropriate tools to investigate adaptation processes.
Inspec keywords: bioelectric phenomena; age issues; user centred design; road traffic; digital simulation; database management systems; behavioural sciences computing; mobile radio; driver information systems; alarm systems; data analysis; automated highways; road safety
Other keywords: safety issues; intersection assistance system; user-centred design; aging; workload based driving assistance systems; inattention prevention; road traffic; database development; situation awareness; ADAS; distraction prevention; behavioural data storage; automated platooning; behavioural data analysis; driving simulators; mobile phone use; driver information processing; adaptive cruise control; learning; ADAPTATION Project; behavioural adaptation; electrophysiological measurement; road safety; information system; driver adaptation; intelligent driver support system; behavioural measurement; ; forward collision warning system; cognitive analysis
Subjects: Mobile radio systems; Road-traffic system control; Data handling techniques; Social and behavioural sciences computing; Traffic engineering computing; General and management topics; General electrical engineering topics; Control engineering computing; Database management systems (DBMS)
ADAPTATION is the short name of 'Drivers' behavioural ADAPTATION over the time in response to ADAS use', which was a Marie Curie Initial Training Network (ITN) funded between 2010 and 2013 under the European Commission Call: FP7-PEOPLE-ITN-2008. The major objective of ADAPTATION was to improve the career perspectives of young researchers by taking part in a research programme aiming to investigating drivers' behavioural adaptation and its underlying processes over time in response to Advanced Driver Assistance Systems (ADAS) use. Within ten PhD projects, accompanied by two post-doctoral projects, ADAPTATION has studied drivers' behavioural adaptation from an integrated perspective working towards an integrated theoretical model of behavioural adaptation.
Adaption processes become important each time a driving situation embodies one or several unfamiliar components. These processes involve a behavioural change emerging into previously established behavioural patterns. Research shows that behavioural changes due to Advanced Driver Assistance Systems (ADAS) are on a continuum ranging from an increase to a decrease in safety. This chapter reviews concepts, theoretical models as well as empirical research regarding these behavioural changes. The literature reviews showed the need for a Model capturing the most relevant factors inducing behavioural adaptation which resulted in the development of a 'Joint Conceptual Theoretical Framework (JCTF) of Behavioural Adaptation in Response to Advanced Driver Assistance Systems'. Alongside, the traditional OECD definition of behavioural adaptation to driving assistance technologies is critically discussed by investigating its main assumptions and its adequacy for current on-market and future ADAS applications.
In recent decades, major technological advances have allowed a large number of Advanced Driver Assistance Systems (ADAS) and In-Vehicle Information Systems (IVIS) to be introduced that intend to improve road and driver safety. With the introduction of ADAS and IVIS, there have also been unintentional potential driver distractions and other safety effects. These effects can be of a short-, medium- or long-term nature. Driving simulator studies and on-road studies offer opportunities to investigate behavioural change as a result of the use of ADAS. In order to design the most suitable approach, it is important to know more about each investigation method, contrasting the methodological approaches with regard to utility, potential research questions, data collection and validity. Moreover, the approaches can also be viewed as complementary for assessing behavioural adaptation effectively and efficiently. This chapter will shed some light on the different methods, pointing out the advantages and disadvantages of realistic driving settings and simulated settings for the investigation of behavioural change.
This chapter focuses on the elaboration and validation of new methods for assessing drivers' higher level cognitive processes reflecting mental models and Situation Awareness (SA). When drivers start using an advanced driver assistance system (ADAS), they acquire a mental model of the system's purpose, function and performance. These mental models should be considered in investigations of behavioural adaptation to ADAS, as they are expected to influence information processing, SA and the selection of appropriate actions. After defining mental models and SA, advantages and disadvantages of existing assessment techniques are presented as well as requirements for new approaches. A newly developed mental model questionnaire is described that was used in a driving simulator study and a field test. SA is measured in real time based on an implicit performance approach using a continuously presented secondary task. Benefits and limitations of both methods are discussed in a final summary.
Adaptive Cruise Control (ACC) is a system that, through the automation of the longitudinal driving task, aims to increase drivers' comfort. Previous studies into ACC showed that behavioural adaptations might occur following its use. Those studies were mainly conducted with drivers who had never used the system before and, to date, little information is available on the behaviour of actual ACC users. Hence, this chapter describes the main findings obtained from focus group discussions and a small scale naturalistic Field Operational Test (nFOT) performed with early adopters. The findings illustrate that behavioural adaptations to the system emerged during both studies and that an improper driver's mental model of the system might be among the triggering causes.
Driving is a complex task, which relies on multiple cognitive and sensory-motor processes to ensure a safe maneuvering of the vehicle. To assist drivers with these processes, a variety of Intelligent Driver Support Systems (IDSS) have been developed. Although most IDSS become very useful over time, they initially often lead to an increase in overall task complexity, which can become excessive, especially for older drivers. Indeed, aging is associated with general changes in cognitive (e.g., slower information processing) as well as sensory-motor (e.g., decrease in sensory-motor sensitivity) functioning. These changes generally cause older adults to experience more difficulties while driving, particularly when additional tasks (e.g., paying attention to traffic signs in an unfamiliar environment or processing information from an unfamiliar IDSS) need to be performed. In this chapter, we review recent research and present a novel empirical study aimed at understanding how younger and older adults learn to drive with IDSS in multitask driving situations. Taking into account the cognitive models of multitask performance and learning, the focus is on what kind of behavioral changes occur with increasing practice with these systems, whether younger and older adults learn at the same rate, and whether they rely on different strategies to cope with increases in task complexity.
Even though road traffic safety strategies and policies have resulted in a significant decrease in crash fatalities, special attention needs to be paid to implementing strategies that will protect older drivers. Over the next few decades, the number of older persons will increase noticeably; and therefore the number of active older drivers will rise as well. Mobility and driving is an important aspect of life, giving people a sense of independence whether being young or old. Older drivers make up a unique age group that faces different difficulties in traffic from younger drivers and thus require special support. Intersection assistance might be a promising approach for supporting older drivers. This has previously been investigated, but only sporadically and in short-term studies. In order to investigate the safety benefits of an intersection assistant for older drivers, and because their learning phase is longer than that of younger drivers, such assessments should involve multiple sessions spread over a prolonged period of time.
Previous researchers have developed hierarchical approaches to describe driver behaviour and have postulated models in which changes at higher levels in a hierarchy affect characteristics at the lower levels. Thus, in research on behavioural adaptation it is important to investigate not only driving performance outcomes (that are mainly represented on lower levels) but also the characteristics of motivational factors that are represented at higher levels. The two motivational factors that have already been considered in past research dealing with the effects of driver assistance systems are trust and acceptance. According to motivational theories, another factor that should now be considered here is perceived risk as it is, theoretically, highly related to potential changes in driving behaviour. In this chapter, the role of motivational factors including perceived risk, perceived behavioural control, norms, attitudes and intentions when drivers use ADAS is discussed. Reference will be made to an extended version of the theory of planned behaviour and to motivational driver behaviour theories.
The increasing use of the mobile phones while driving raises a safety concern due to its distractive potential and its consequent effects on crash risk. The way phone use affects driving depends on the usage behaviour of the driver. First, drivers can actively regulate their exposure to phone interactions. Second, they can make choices on the strategic level of the driving task so as to ensure the compatibility of the phone use with driving. Strategies can aim at lowering the demands in one of the concurrent tasks, for example, by using assistance systems or a hands-free device. Third, the phone use while driving can impair the driving performance because of the distractive nature of the dual-task situation. With the aim to mitigate this impairment and to uphold an acceptable driving performance, drivers can deliberately adapt their behaviour on the tactical level of the driving task. These driver adaptation strategies to mobile phone use are discussed along with their actual implementation and effectiveness. An extensive literature review has been complemented with findings from naturalistic driving studies and in-depth interviews carried out within the ADAPTATION project. A discussion on the potential safety impact of phone use and drivers' adaptation to it concludes this chapter.
Advanced Driving Assistance Systems (ADAS) support the driver during highly automated and continuous driving tasks (e.g. adaptive cruise control) and provide warnings in potentially dangerous situations (e.g. forward collision warning). As ADAS do not work flawlessly, andbecause of legal issues, the driverneeds to supervise these systems in order to intervene when necessary. This means that the driver has to continually monitor information given by the systems, even though the human brain is not optimised for prolonged and monotonous control tasks, which can be interrupted by highly critical situations at any point in time. As has been shown by several validations of the Yerkes-Dodson law, the driver is vulnerable to 'out-of-the-loop' problems in low workload conditions and may not realise quickly enough that a situation is critical. Conversely, in high workload conditions the driver loses sight of the control task or needs more time to initiate an appropriate reaction in a dangerous situation. This chapter describes how a new theory has been developed, and a compensation strategy for the use of adaptive cruise control in high workload conditions has been designed and evaluated in a simulator and in on-road conditions. The technological possibility of detecting high workload conditions using physiological data has been established; the workload when the drivers' reaction time is influenced by a secondary task has been evaluated. In addition, as the usage of a system is strongly dependent on behaviour and impressions, the chapter describes how acceptance, as well as awareness of the system, has been examined.
Development of Behaviour-Based Safety (BBS) and Advanced Driver Assistance Systems (ADAS) has been carried out largely independently of each other. Although both approaches have the same goal of improving traffic safety, they operate at different timescales to achieve accident prevention. This chapter examines how both ADAS and BBS approaches can be combined into a more holistic framework and applied to preventing distraction and inattention. The combination of ADAS (immediate feedback) with BBS (long-term feedback) is illustrated by using the analogy of 'team play'. Both ADAS and BBS are players of the same team on the 'accident prevention playing field' and united, they become a better team. With the BBS-ADAS team, traffic safety has the opportunity to advance into an entirely new league.
This chapter discusses Forward Collision Warning Systems (FCWSs), describing the characteristics and the functions of some of the systems currently on the market and presents an overview of behavioural studies evaluating the effectiveness of these systems on road and in simulators. Results are also presented from recent studies using electroencephalography and the associated event related potentials (ERPs) allowing, through the analysis of brain activity, a more in-depth understanding of the nature of the cognitive processes in the context of FCWSs. These studies address three important questions: 1. Are FCWSs as effective as they are expected to be when drivers are distracted? 2. What are the consequences of driving with a system that is not completely reliable? 3. Is there any behavioural adaptation to the FCWSs over their use in time? Are the consequences of this adaptation positive or negative? The chapter shows that FCWSs provide potential benefits for road safety, but certain factors such as the attentional state of drivers and the reliability level of the system can mitigate its effectiveness.
Automated driving is a realistic future scenario that will arise from a combination of different Advanced Driver Assistance Systems (ADAS) that are gradually being introduced in vehicles to support aspects of the driving task. As the traffic fleet becomes more varied and equipped vehicle drivers (EVDs) are mixed with unequipped vehicle drivers (UVDs), it becomes important to ensure that this evolution is safe for EVDs and remains safe for UVDs. The aim of this chapter is therefore to introduce a new research field investigating the impact of EVDs on the UVDs with a particular emphasis on one scenario: the behavioural adaptation of the UVDs to the short time headway (THW) kept by automated vehicles. The chapter commences with an overview of the development of automated systems and their increasing role within the driving task towards the implementation of fully automated driving. Finally, some guidelines are provided for conducting research in this new area.
Modelling scenarios on driving simulators is a critical and complex task for behavioural researchers. It requires specific technical and programming skills, for which researchers are typically not formally trained. The main reason for this complexity is the lack of User-Centred Design (UCD) in scenario authoring tools, including an unintuitive and user-unfriendly interaction environment. This could account for some of the challenges faced by behavioural researchers in achieving their objectives with driving simulators. In this chapter, we discuss the problem in detail and propose a user-centred solution, which has been evaluated by users. We also propose a meta-language and an interoperability framework to generalise our solution, so that our approach could be used on most driving simulators.
This chapter presents results of one of the post-doctoral projects conducted in the framework of the ADAPTATION project. This study aimed to develop and implement a database and data analysis protocol enabling the storage, joint access, individual analysis and integration of different types of data that are collected during driving simulator and field studies. Such a tool should allow researchers to gather large amounts of data in a unique place, from which it can be managed and retrieved efficiently. The potential of a database for exchanging data with other research institutes and for conducting secondary analysis is discussed. This is one of the first databases of its kind in Europe for archiving Human Factors in the field of road transportation research.
Within the ADAPTATION project a series of individual research studies was carried out on the basis of a Joint Conceptual Theoretical Framework. An overview of the findings that were obtained is provided in this chapter, combining the results so as to conclude on three topics that are discussed in the context of implementation and deployment of Advanced Driver Assistance Systems (ADAS): the roles of system characteristics, driver populations and user support strategies. Factors that increase or diminish the expected benefits of ADAS are inferred both from changes detected in driving behaviour (induced by ADAS use) and from subjective system evaluations by drivers. Furthermore, a synthesis of the critical review of methods to study behavioural adaptation in the driving context is given and the two tools developed within the project are discussed. The synthesis of new knowledge allows for conclusions that go beyond the individual studies and confirms the benefit of carrying out research under a joint framework.
Based on the knowledge gained within the ADAPTATION project and synthesised in Chapter 16, this concluding chapter gives three kinds of recommendations. First, it deduces future research prospects concerning drivers' behavioural adaptation in response to ADAS use and on its underlying processes. Next, methodological recommendations are given following the identification of challenges related to this kind of research. Finally, the outcome of the ADAPTATION project is translated into suggestions concerning how to increase the expected benefits of new or existing ADAS on driving safety.