With continuous advancements in autonomous driving technology, environmental perception has attracted significant research interest. Millimeter-wave (mmWave) radars are essential components in autonomous driving systems, offering strong interference immunity, reliable performance across various weather conditions, and highly accurate target detection. Through a thorough literature survey, this review compares the current developments and technological innovations of mmWave radars applied in environmental perception. Besides, it delves into the basic operating mechanisms of mmWave radar and evaluates its critical performance indicators, while investigating its applications in target detection, ranging, and obstacle identification. The results indicate that while mmWave radar significantly boosts perception accuracy and response speed, it still faces challenges such as limited resolution, data processing latency, and high costs. Overall, integrating multi-sensor data fusion with AI-driven signal processing emerges as a promising direction for further enhancement. By combining complementary sensor data and leveraging advanced AI algorithms, this method enables more efficient, adaptive, and reliable responses to complex driving environments.
Zihan Zheng (Wed,) studied this question.
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