Intelligent scheduling for in-car notifications.

The process of driving a car involves a cognitive load that varies over time. Additional load comes from secondary factors not directly associated with the driving process, including navigation devices, entertainment systems and the car’s own warnings. In this paper, we present a framework for intelligent scheduling of in-car notifications based on the driver’s estimated cognitive load. As the single channel for communication, it reschedules the notifications using a priority queue, and relays them to the driver based on the urgency of the notification and the overall estimated cognitive load being experienced by the driver at any given moment. We evaluate our system using a dataset collected from a car’s CAN bus during multiple onroad trials and show that our proposed approach reduces the number of simultaneous calls on the driver’s attention during the driving task. We also demonstrate that our intelligent scheduling significantly reduces the maximum cognitive load experienced by the driver and the frequency with which high loads occur

Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data

Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus delivers adaptive human machine interaction with reduced impact on the primary task of driving. In this paper, we tackle the problem of workload estimation from driving performance data. First, we present a novel on-road study for collecting subjective workload data via a modified peripheral detection task in naturalistic settings. Key environmental factors that induce a high mental workload are identified via video analysis, e.g. junctions and behaviour of vehicle in front. Second, a supervised learning framework using state-of-the-art time series classifiers (e.g. convolutional neural network and transform techniques) is introduced to profile drivers based on the average workload they experience during a journey. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload. This computationally efficient and flexible method can be easily personalised to a driver (e.g. incorporate their inferred average workload profile), adapted to driving/environmental contexts (e.g. road type) and extended with data streams from new sources. The efficacy of the presented profiling and instantaneous workload estimation approaches are demonstrated using the on-road study data, showing $F_{1}$ scores of up to 92% and 81%, respectively.Comment: Accepted for IEEE Transactions on Intelligent Vehicle

Intelligent scheduling for in-car notifications

© 2017 IEEE. The process of driving a car involves a cognitive load that varies over time. Additional load comes from secondary factors not directly associated with the driving process, including navigation devices, entertainment systems and the car's own warnings. In this paper, we present a framework for intelligent scheduling of in-car notifications based on the driver's estimated cognitive load. As the single channel for communication, it reschedules the notifications using a priority queue, and relays them to the driver based on the urgency of the notification and the overall estimated cognitive load being experienced by the driver at any given moment. We evaluate our system using a dataset collected from a car's CAN bus during multiple on-road trials and show that our proposed approach reduces the number of simultaneous calls on the driver's attention during the driving task. We also demonstrate that our intelligent scheduling significantly reduces the maximum cognitive load experienced by the driver and the frequency with which high loads occur