A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction

Driver distraction is a major challenge in road traffic and major cause of accidents. Vehicle industry dedicates increasing amounts of resources to better quantify the various activities of drivers resulting in distraction. Literature has shown that significant causes for driver distraction are tasks performed by drivers which are not related to driving, like using multimedia interfaces or glancing at co-drivers. One key aspect of the successful implementation of distraction prevention mechanisms is to know when the driver performs such auxiliary tasks. Therefore, capturing these tasks with appropriate measurement equipment is crucial. Especially novel quantification approaches combining data from different sensors and devices are necessary for comprehensively determining causes of driver distraction. However, as a literature review has revealed, there is currently a lack of lightweight frameworks for multi-device integration and multi-sensor fusion to enable cost-effective and minimally obtrusive driver monitoring with respect to scalability and extendibility. This paper presents such a lightweight framework which has been implemented in a demonstrator and applied in a small real-world study involving ten drivers performing simple distraction tasks. Preliminary results of our analysis have indicated a high accuracy of distraction detection for individual distraction tasks and thus the framework’s usefulness. The gained knowledge can be used to develop improved mechanisms for detecting driver distraction through better quantification of distracting tasks.