The research to date on AVs has tended to focus on the safety aspect rather than
the comfort of the passengers. It is obvious that safety is the most important element
and should always come first, however in order to facilitate their rapid uptake and deployment, AVs should ensure that occupants feel both safe and comfortable. As it was discussed previously, the comfort is a subjective term and is influenced by different factors. One of the most important factors is the deceleration behaviour. It is, however, unclear how deceleration profiles, values and durations affect the level of occupants’ comfort.
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Through an in-depth literature review, the key points of the research gap were extracted:
• Lack of examining situational factors and the co-occurrence of different affecting factors
Previous studies of deceleration behaviour and comfort during braking have examined factors that are related either to the driver (Loeb et al., 2015), i.e. age, gender and experience; or to the vehicle, i.e. kinematic factors such as the initial speed (Haas et al., 2004; Kazumoto et al., 2006). There is a lack of research in studying situational factors such as the reason for braking, and the traffic density at the moment of braking which still could play an important role in the driver’s decisions (apply and release the brake harder or softer; apply the brake for a longer or shorter period of time). In addition, no research has taken into consideration all those factors at once, which demands multilevel analysis. So far, this method has only been applied to social, education and medical sectors. Therefore, it is not clear yet the impact of all these factors (driver, kinematics, situational) on the deceleration behaviour and specifically on the deceleration profiles, values and durations and how they relate to different roadway infrastructure and traffic operational conditions.
• Dearth in research on detecting deceleration events
Moreover, through the literature, there have been different thresholds to evaluate passenger’s comfort during the driving task and to detect especially the braking events (Naito et al., 2009; Wu et al., 2009). So, an overall method and thresholds are needed to be established in order to detect and analyse deceleration events.
Also, the need to apply thresholds that have been tested to passengers’ comfort and create the appropriate comfort levels is apparent. Establishing the passengers’ preferences is an important item for the future research agenda (Le Vine et al., 2015a). Moreover, it is important to examine which factors affect those comfort levels and increase the likelihood of an event to become very uncomfortable which might lead to dissatisfaction or even motion sickness for the passenger of the AV.
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So far the methods that have been used the most in studying deceleration and general driver behaviour in order to obtain the necessary data are self-reported methods (French et al., 1993; Ulleberg and Rundmo, 2003; Taubman-Ben-Ari et al., 2004) and simulators (Goodrich et al., 1999b; Lefèvre, et al., 2015b; Yusof and Karjanto, 2015). Both of those methods can provide useful data but not so trustworthy since it is not certain to what degree people will be honest when completing a questionnaire or if they will behave exactly as they do in the real road environment when being in a simulator. On the contrary, studies on drivers’ braking behaviour observed in normal driving by using naturalistic data that can overcome the aforementioned disadvantages, are limited.
Last but not least, a considerable amount of literature has been published on implementing the driving behaviour into AVs (Kuderer et al., 2015; Lefèvre, et al., 2015b). Nevertheless, they did not suggest any general recommendation to the autonomous cars’ designers since these studies used mostly learning-based methods (learning from demonstration) for a specific driver or a specific situation.
This study aims to fill in these knowledge gaps by analysing drivers’ braking behaviour from normal driving using naturalistic data in different scenarios (i.e. different road infrastructure and different road conditions). It will focus on discovering the relationship between the braking behaviour and its influencing factors by taking into consideration as many factors as possible, such as human factors, trip factors, situational and kinematics ones. In addition, the situational, kinematic and driver factors that may affect the comfort of the deceleration event will be examined and suggestions will be made to avoid the situations that increase the discomfort of the event.
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3 Data Description and Pre-processing
This work is a data-driven PhD project in which the availability of high-quality data is vital for ensuring both the validity and clarity of the study. Moreover, data quality is important in studying driver behaviour, especially in normal real-world driving conditions. The data should describe the situation while drivers are driving naturally without taking into consideration that they are monitored in order to be completely representative of normal driving. Therefore, this research uses naturalistic driving data.
This chapter begins with a review of the data collection approaches used to analyse driving behaviour. Then, it describes the features of the datasets which were employed in the analysis. The three projects, from which the data were obtained, are described. Moreover, a part of the chapter is dedicated to the examination of the data.