ALT: Towards Automating Driver License Testing using Smartphones - Nambi, Akshay; Mehta, Ishit; Ghosh, Anurag; Lingam, Vijay; Padmanabhan, Venkat (ACM SenSys, 2019)
Can a smartphone administer a driver license test? We ask this question because of the inadequacy of manual testing and the expense of outfitting an automated testing track with sensors such as cameras, leading to less-than-thorough testing and ultimately compromising road safety. We present ALT, a low-cost smartphone-based system for automating key aspects of the driver license test. A windshield-mounted smartphone serves as the sole sensing platform, with the front camera being used to monitor driver’s gaze, and the rear camera, together with inertial sensors, being used to evaluate driving maneuvers such as parallel parking. The sensors are also used in tandem, for instance, to check that the driver scanned their mirror during a lane change. The key challenges in ALT arise from the variation in the subject (driver) and the environment (vehicle geometry, camera orientation, etc.), little or no infrastructure support to keep costs low, and also the limitations of the smartphone (low-end GPU). The main contributions of this paper are: (a) robust detection of driver’s gaze by combining head pose and eye gaze information, and performing auto-calibration to accommodate environmental variation, (b) a hybrid visual SLAM technique that combines visual features and a sparse set of planar markers, placed optimally in the environment, to derive accurate trajectory information, and (c) an efficient realization on smartphones using both CPU and GPU resources. We perform extensive experiments, both in controlled settings and on an actual driving test track, to validate the efficacy of ALT.