Abstract
Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.
Highlights
Vehicle position tracking studies are crucial for accurately estimating distances
Target channel lidars have fewer beams reflected, and measuring the distance with a lidar beam in a small necessary. This distance was measured by averaging the lidar data measured with the point cloud vehicles at close range can be measured relatively accurately if the center area is averaged after area was necessary
The error was calculated using is, the data error of the unprocessed lidar was less than cm, and the value measured by the lidar with a reliability function was designed to reflect the distance characteristics
Summary
Vehicle position tracking studies are crucial for accurately estimating distances. Position tracking is used in autonomous vehicle research when solving situations using detection alone is difficult. These types of studies use lidar sensors and radar cameras for detection and recognition. A recent study made use of deep learning to estimate positions; this method produced errors when a vehicle passed by on a slope [6]. Camera sensors have difficulties recognizing objects and estimating positions at night, and sensor fusion compensates for these problems by reducing measurement distance errors to improve detection [7,8,9]. A Kalman filter was used to track the distance vehicle by combining lidar and radar data.
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