Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation

Abstract

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

“…The authors in [ 27 , 36 ] illustrate this point that a non-parametric modelling approach is able to model sensor range, occlusion, latency, ghost objects, and object loss without explicit programming, and can be used efficiently in real-time simulation. The same concept is developed in [ 26 , 65 ], where the geometric information of the target is transformed into the sensor model, and then the signal noise and statistically based signal loss are superimposed on the original signal. The method described above has provided good estimation and modelling of relative distance, velocity, and other sensor-specific information.…”

Advanced Sensing and Control for Connected and Automated Vehicles

“…This Special Issue of Sensors aims at reporting on some of the recent research efforts on this increasingly important topic. The 12 accepted papers in this Issue cover vehicle position estimation [1], vehicle dynamic parameters estimation [2], cooperative collision warning systems [3], small object detection [4], impact identification of the driver's driving performance on executive control function [5], hybrid path planning for autonomous driving [6], trajectory tracking for autonomous driving [7], vehicle stability control [8], vehicle stability and ride comfort control [9], urban platooning protocol design for platoon [10], path planning algorithm for platooning [11], and self-driving architecture design for CAV platoon [12].…”

“…In [1], a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems is proposed. An accuracy model considers the different error characteristics depending on the zone.…”

“…In summary, there is a huge potential for CAV in collision avoidance, safety improvement and driving stability improvement. The papers gathered in this Special Issue contributed by proposing solutions to the general problem of state and/or parameter estimation [1][2][3], obstacle detection [4], driver behavior estimation [5], and/or classical academic problems such as path planning [6], path tracking [7], and stability control for autonomous driving [8,9] and/or by suggesting applications of the combined use of sensors and advanced algorithms to platooning [10][11][12], thus showing the theoretical challenges and practical interest of this research topic. Finally, we wish to thank the authors, reviewers, and journal staff for their commitment and effort, which made it possible to complete this Special Issue on time.…”

A Novel Triple Radar Arrangement for Level 2 ADAS Detection System in Autonomous Vehicles

“…Some researchers have proposed algorithmic procedures to enhance the accuracy of detection results obtained by Radars. In [17], the researchers compared the radar detection and ground truth data, an accuracy model for a radar system is developed. Then radar latency is obtained according to the relative velocity.…”