An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and efficiency as well as comfort for drivers in the driving process. Recent studies have noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause distraction which would affect its usage and even lead to safety issues. Current understanding of these issues is limited to the context-dependent nature of such systems. This paper reports the development of a holistic conceptualisation of how drivers interact with ADAS and how such interaction could lead to potential distraction. This is done taking an ontological approach to contextualise the potential distraction, driving tasks and user interactions centred on the use of ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used to deduce rules for identifying distraction from ADAS and informing future designs

Rearview Video System as Countermeasure for Trucks’ Backing Crashes: Evaluating the System’s Effectiveness by Controlled Test

In general, the operation of large trucks involves many different types of maneuvers. The backing maneuver, in particular, requires a higher level of driver attention because of the limited rear view. A growing number of trucks in the United States are equipped with a rearview video system (RVS) that can help the driver see much of the area behind the vehicle. An RVS consists of one or more cameras and one monitor. It is expected that an RVS can help drivers reduce potential backing crashes. To evaluate the effectiveness of the system, this study performed a controlled driver test with 45 truck drivers. The test used three backing maneuvers and a pedestrian dummy for observation of potential crashes. The results showed that the use of an RVS increased the stop rate of the drivers during the straightline backing maneuver by 46.7%, which could be interpreted as an increase in the odds of avoiding potential backing crashes during the backing maneuver. The stop rate increased 4.4% and 17.8% for the offset right backing and alley dock backing maneuvers, respectively. Driver age, commercial driving experience, and experience with an RVS showed no statistical association with the increased stop rate, which means an RVS can be adopted by drivers quickly. In general, drivers showed positive attitudes toward using an RVS, and more than 90% of respondents agreed that an RVS could reduce the rear blind spot for large trucks

Effects of cognitive load on driving performance: The cognitive control hypothesis

Objective: The main objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. Background: While there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. Method: Based on existing models of driver attention, an explanatory framework was outlined. This can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving sub-tasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted where existing results were re-interpreted based on the proposed framework. Results: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on non-practiced or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load while the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. Conclusion: Effects of cognitive load on driving are strongly selective and task-dependent. Application: The present results have important implications for the generalization of results obtained from experimental studies to real world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes