Abstract
Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.
Highlights
In recent years, vehicles with advanced driver assistance system (ADAS) have helped reduce traffic accidents and become a research focus at home and abroad
E radar target tracking algorithm gets the lateral distance between the target-vehicle and the ego-vehicle through the radial distance and azimuth information. en, the ego-vehicle can identify the lane relationship with the target-vehicles. e low resolution of radar azimuth leads to the erroneous lateral distance. e ego-vehicle has different driving courses on a straight road and a curved road. at is, the on-board millimeter-wave radar has yaw motion when the ego-vehicle runs on the curved road
MicroAutoBoxII is connected to RT3000 through controller area network (CAN) and gets the lateral velocity, longitudinal velocity, and yaw rate information of the egovehicle measured by RT3000. e ego-vehicle is equipped with a 77 GHz millimeter-wave radar, the target measurement data of which is open to the public, provided by a company, in the middle of the front bumper. e millimeter-wave radar is provided with two-way CAN
Summary
Vehicles with advanced driver assistance system (ADAS) have helped reduce traffic accidents and become a research focus at home and abroad. At is, the on-board millimeter-wave radar has yaw motion when the ego-vehicle runs on the curved road. Erefore, as the MHT algorithm will need large computing resources in millimeter-wave radar target tracking, it is hard to meet the needs of real-time requirement. In order to eliminate the influence of the yaw motion of the ego-vehicle on the radar measurement data during driving on the curved road, motion compensation is required for the original target information detected by the radar. Since the statistical parameters of the observation noise in on-board millimeter-wave radar are often unknown and time-varying, the adaptive extended Kalman filter (AEKF) algorithm is adopted to calculate the statistical parameters of the noise.
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