Bidirectional LSTM-Based Overhead Target Classification for Automotive Radar Systems
Chanul Park, Seungheon Kwak, Hojung Lee, Seongwook Lee
Abstract
In this article, we propose a method of classifying the overhead target in automotive radar systems. The conventional automotive radar systems struggle to distinguish overhead road structures (e.g., bridges, overpasses, and traffic signs) and ground-level targets (e.g., vehicles and pedestrians) due to the lack of elevation resolution. Consequently, the forward collision-avoidance assist (FCA) system or the autonomous emergency braking (AEB) system, may perceives detected overhead road structures as being on the road. Therefore, unnecessary operation of the FCA or AEB may occur. Our proposed method exploits the different patterns of changes in the estimated range, relative velocity, and signal amplitude as the radar approaches the overhead structure and ground-level targets. These patterns can be used to classify overhead structures by designing the appropriate classifier. Therefore, additional antenna elements arranged in the vertical direction required for elevation angle estimation are unnecessary. First, we obtain the radar signals by measuring the metal reflector installed at different heights. Then, by processing the received signal, we generate the input data for the proposed bidirectional long short-term memory (LSTM)-based height classifier. Our proposed method can classify the installation height of the reflector with an average accuracy of 98.18% within the range of 13 m, and an average accuracy of 94.97% within the range of 20 m in a complicated environment.