Analysis of object detection problems in autonomous driving systems based on radar dat
Anton D. Kuzin , Vladimir V. Debelov , Denis V. Endachev
Izvestiya MGTU MAMI ›› 2024, Vol. 18 ›› Issue (4) : 278 -288.
Analysis of object detection problems in autonomous driving systems based on radar dat
Background: Modern autonomous driving systems impose high demands on the quality of object detection and classification in the surrounding environment. Radar systems, due to their resilience to adverse weather conditions and ability to measure velocity, play a crucial role among the object and obstacle detection systems used in autonomous vehicles. However, various technical issues related to noise, incorrect classification, and errors in determining object characteristics can hinder the operation of these systems.
Objective: Identification and analysis of the main problems of object detection and classification based on radar data, and assessment of their impact on the safety and performance of autonomous driving systems.
Methods: In this study, experimental data collection was carried out in city traffic conditions using the ARS 408 automotive radar. Modern software tools including the Robot Operating System (ROS) were used to analyze and process the data. Detection quality evaluation metrics such as IoU, Precision, Recall and F1-score were applied in the study.
Results: Within the study, the methodology for radar system data analysis and identification of the main problems encountered during object detection, including the effects of noise, classification errors and object size biases, is developed. Approaches to assessment of quality of the detection algorithms are proposed and a comparative analysis of the convergence of object detection data under various conditions is carried out.
Conclusions: The results highlight the main problems of object detection by radar systems and help to assess the quality of current algorithms. The practical significance of the study lies in analyzing the weaknesses of object detection systems and providing data for algorithm improvement, which can enhance the safety of autonomous vehicles.
electrotechnical facility / autonomous driving / object detection systems / radar data / detection instability / data processing algorithms / environment perception problems
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