With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

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Real driving scenarios, due to occlusions and disturbances, provide disordered and noisy measurements, which makes the task of multi-object tracking quite challenging. Conventional approach is to find deterministic data association; however, it has unstable performance in high clutter density. This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker (PTMOT), which integrates Poisson multi-Bernoulli mixture (PMBM) filter with confidence of tracklets. The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking (MOT) and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis. It consists of two key parts. First, the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measurements. Second, the confidence of tracklets is smoothed through a smoothing-while-filtering approach. Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.