This study uses a low-density solid-state flash lidar for estimating the trajectories of road vehicles in vehicle collision avoidance applications. Low-density flash lidars are inexpensive compared to the commonly used radars and point-cloud lidars, and have attracted the attention of vehicle manufacturers recently. However, tracking road vehicles using the sparse data provided by such sensors is challenging due to the few reflected measurement points obtained. In this paper, such challenges in the use of low-density flash lidars are identified and estimation algorithms to handle the same are presented. A method to use the amplitude information provided by the sensor for better localization of targets is evaluated using both physics-based simulations and experiments. A two-step hierarchical clustering algorithm is then employed to group multiple detections from a single object into one measurement, which is then associated with the corresponding object using a Joint Integrated Probabilistic Data Association (JIPDA) algorithm. A Kalman filter is used to estimate the longitudinal and lateral motion variables and the results are presented, which show that good tracking, especially in the lateral direction, can be achieved using the proposed algorithm despite the sparse measurements provided by the sensor.
|Original language||English (US)|
|Journal||Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME|
|State||Published - Mar 19 2021|
Bibliographical noteFunding Information:
This research was supported by the Ministry of Skill Development and Entrepreneurship, Government of India, through the Grant No. EDD/14-15/023/MOLE/NILE.
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