An adaptive driving-assistance system to support situation awareness for driving in an unfamiliar traffic system

Driving under unfamiliar regulation using unfamiliar vehicle configuration contributes to an increased number of traffic fatalities, injuries, and property damage. Situation awareness (SA) is the perception of environmental elements and events with respect to time and space, the comprehension of their meaning, and the projection of their future status. Cognitive flexibility (CF) is the mental ability to switch between thinking about two different concepts simultaneously. There are two subcategories of cognitive flexibility: task switching and cognitive shifting. The driver needs to apply their prior knowledge to a new driving situation in order to drive safely. This ability is a demonstration of CF. Prior research has found that CF is influenced by the degree of handedness. That is, in tasks that require it, left/mixed-handed people show superior cognitive flexibility over right-handed people. Existing work using driving-assistance systems to help with situation awareness focuses predominantly on assisting the driver by presenting information that will allow them to make the proper decision and driving responses. However, current driving-assistance systems are inadequate for supporting situation awareness when driving under unfamiliar traffic regulations. The aim of this research is to develop a driving-assistance system that adapts the provided information based on the handedness degree in a way that supports the situation awareness for drivers who drive in an unfamiliar traffic regulation. Unfamiliar traffic regulation (UFTR) in this thesis refers to driving under unfamiliar traffic regulation, namely a keep-left traffic regulation using a keep-right drive vehicle for those who are only familiar with driving in a keep-right traffic regulation using a keep-left drive vehicle. This research includes three studies. The first study is a quantitative study, which aims to examine the feedback requirements in terms of usability and presentation mechanism. The results enhanced the ability of the driver to capture the provided information, understand it and plan appropriately for the required driving reaction. The second study is an empirical study using a driving simulator. It aims at exploring the relationship between the degree of handedness and driving performance while driving in an unfamiliar traffic regulation, particularly at roundabouts and intersections. Left/mixedhanded drivers made significantly fewer errors that could be attributed to a lack of CF, than did strong right-handed drivers. Findings from this study provide the data and feedback for structuring the implementation of the VEHand prototype on a driving simulator and a smart device. VEHand is a driving-assistance system provides the drivers with useful feedback based on the handedness degree and it is specialized for driving under unfamiliar traffic regulation. According to the results from the first empirical study, we developed the implementation of the VEHand prototype. The third study is an empirical study, which is conducted in a driving simulator. It aims to evaluate the effectiveness of VEHand for improving the driving performance and therefore improving the overall SA. This evaluation study is based on a cross-over study design, which reduces the learning effect. The main results of this study indicated that VEHand helped strong-right handed drivers to significantly reduce driving errors at turn-left roundabout and intersection and turn-right intersection. Furthermore, VEHand significantly assisted strong-right handed drivers to enter the upcoming roundabout from the correct direction around the island of roundabout and intersection from the correct side of the road.