Sense as you go: A context-aware adaptive sensing framework for on-road driver profiling

The realization of next-generation intelligent transportation services primarily depends on the rich data that could effectively capture the on-road and in-vehicle conditions in real time. Road safety being one of the most critical objectives for such services, received significant research interest in the past. Smartphone-based inertial sensing has proved to be quite promising in identifying potential anomalies in road conditions or driving patterns. However, the data generated from the onboard Inertial Measurement Unit are exposed to high noise due to mobility and subjective diversity. Moreover, a large amount of data produced at a higher sampling rate directly impacts on the battery life of such resource-constrained devices as well as the privacy of the concerned user. To address this set of challenges, we introduce a novel framework for mobile devices with the objective to support 'sensing on a need' basis. The proposed framework AdaSift has three key features: (1) detects the relevant context signifying a window of important data points in real-time, (2) filters out irrelevant data at the source itself and thus reduces the associated overhead significantly; and (3) effectively identify a driver's profile based on the unique behavioral signature traced by the filtered data. We evaluated the performance of AdaSift by implementing the framework on an Android smartphone and conducting a user study with 8 drivers. The experimental results show that the use of AdaSift could achieve up to accuracy in on-road driver profiling while reducing the energy consumption footprint of the smartphone by up to .