With the rapid advancement of intelligent transportation technologies, market demands for advanced driver assistance systems (ADAS) have grown increasingly stringent. Environmental perception, as the core technical component of autonomous driving systems, faces complex real-world challenges. Traditional single-modality perception solutions exhibit inherent limitations, prompting the emergence of multi-modal perception systems. Among these, millimeter-wave (mmWave) radar-a cost-effective and high-performance sensor-transcends human perceptual constraints and is pivotal in ADAS. This paper summarizes recent technological progress in fusion algorithms integrating mmWave radar with diverse sensors for ADAS applications, with a focused analysis of fusion mechanisms and functional roles between mmWave radar and visual sensors, LiDAR, and infrared sensors. The study reveals intrinsic connections and evolutionary patterns across different technical pathways. The research aims to provide theoretical references for designing intelligent driving perception systems and facilitate breakthroughs in multi-modal fusion challenges.
Chao Chen (Tue,) studied this question.
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