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arxiv: 1807.05412 · v1 · pith:ZF44DEWGnew · submitted 2018-07-14 · 📡 eess.SP

ViLDAR - Visible Light Sensing Based Speed Estimation using Vehicle's Headlamps

classification 📡 eess.SP
keywords lightspeedsystemsvehiclecurveddetectionestimationlidar
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The introduction of light emitting diodes (LED) in automotive exterior lighting systems provides opportunities to develop viable alternatives to conventional communication and sensing technologies. Most of the advanced driver-assist and autonomous vehicle technologies are based on Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use radio frequency or laser signals, respectively. While reliable and real-time information on vehicle speeds is critical for traffic operations management and autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies especially in curved road scenarios where the incidence angle is rapidly varying. In this paper, we propose a novel speed estimation system so-called the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing visible light variation of the vehicle's headlamp. We determine the accuracy of the proposed speed estimator in straight and curved road scenarios. We further present how the algorithm design parameters and the channel noise level affect the speed estimation accuracy. For wide incidence angles, the simulation results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight and curved road scenarios. A provisional patent (US#62/541,913) has been obtained for this work.

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  1. Speed estimation evaluation on the KITTI benchmark based on motion and monocular depth information

    cs.CV 2019-07 unverdicted novelty 3.0

    Combining monocular depth and optical flow predictions from deep networks yields ego-vehicle speed estimates with RMSE below 1 m/s on KITTI after approximating one scale factor.