TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

Zheng, Lianqing; Ma, Zhangfeng; Zhu, Xichan; Tan, Bin; Li, Sen; Long, Kai; Sun, Weijian; Chen, Sihan; Zhang, Lu; Wan, Mengyue; Huang, Libo; Bai, Jie

doi:10.1109/itsc55140.2022.9922539

Cited by 54 publications

(8 citation statements)

References 15 publications

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“…• Experiments show that LXL outperforms state-of-the-art models on the View-of-Delft (VoD) [10] and TJ4DRadSet [24] datasets by 6.7% and 2.5%, respectively, demonstrat-ing the effectiveness of LXL. In addition, comparisons of different feature lifting and radar assistance strategies are made through ablation studies, showing the superiority of the proposed view transformation module.…”

Section: Introductionmentioning

confidence: 89%

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

Xiong,

Liu,

Huang

et al. 2024

2024 IEEE Intelligent Vehicles Symposium (IV)

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Section: Introductionmentioning

confidence: 89%

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

Xiong,

Liu,

Huang

et al. 2024

2024 IEEE Intelligent Vehicles Symposium (IV)

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“…Moreover, there are additional Radar datasets for autonomous driving, such as SeeingThroughFog [49], AIODrive [50], VoD [51], TJ4DRadSet [52], K-Radar [53] and aiMotive [54].…”

Section: Oncementioning

confidence: 99%

Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection

Alaba,

Gurbuz,

Ball

2024

WEVJ

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The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research.

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“…The performance parameters of the 4D radar sensor are shown in Table 1. The TJ4DRadSet [27] dataset was collected and is used for the algorithm analysis. As shown in Figure 4, the collection platforms of the dataset are displayed.…”

Section: Experiments Setupmentioning

confidence: 99%

Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

Tan

Zhu

et al. 2023

Remote Sensing

Self Cite

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This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.

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TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving

Cited by 54 publications

References 15 publications

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

Emerging Trends in Autonomous Vehicle Perception: Multimodal Fusion for 3D Object Detection

Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

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