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.

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.

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.

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.

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.

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.

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.

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.