A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles

Dai, Deyun; Chen, Zonghai; Bao, Peng; Wang, Jikai

doi:10.3390/wevj12030139

Cited by 17 publications

(6 citation statements)

References 82 publications

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“…For image-based schemes, such as designing some depth features based on stereo point clouds to generate some 3D candidate boxes (3DOP) or generating 3D candidate regions (Mono3D) using ground planes and some semantic information, some hand-crafted features need to be added to compensate for the lack of accurate depth information [8]. However, these specific hand-crafted features and a single RGB image will limit the expansion of the scene and the effective learning of 3D spatial information by neural networks.…”

Section: Image-based 3d Detection Methodsmentioning

confidence: 99%

Research advanced in object detection of autonomous driving based on deep learning

Feng

Wang²,

Zhou³

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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Object detection has always been one of the hot tasks in the computer vision community, whose goal is to accurately and efficiently identify and locate a large number of predefined categories of object instances from the image. With the widespread application of deep learning, the accuracy and efficiency of object detection have been greatly improved, and thousands of methods have be proposed to improve the detection accuracy and speed. In this paper, we first introduce and analyze the representative methods from the 2D object detection to 3D object detection, and then give an exhaustive introduction to common datasets and conduct a comparative performance analysis of the core ideas of different algorithms. Also, we summarize the existing problems in the detection research field and predict the solutions to these problems in the future.

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Section: Image-based 3d Detection Methodsmentioning

confidence: 99%

Research advanced in object detection of autonomous driving based on deep learning

Feng

Wang²,

Zhou³

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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“…While there has been extensive research on object detection in on-road scenarios, and thus the corresponding public datasets, particularly in the context of autonomous driving applications [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], there is a noticeable scarcity of studies addressing object detection in off-road environments due to data limitations. For instance, a study by [26] evaluates a person detection algorithm in off-road environments, considering occlusion and non-standard poses.…”

Section: Object Detection In Off-road Environmentmentioning

confidence: 99%

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Baek,

Yu,

Son

et al. 2024

Sensors

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Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

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“…[25] gives a detailed insight into the recent advancements in object detection and discusses the use of camera and Lidar data for 2D and 3D object detection in images and point cloud data. The perception module and its significance in a full stack of ADS is discussed in [26]. 3D object-detection is a protrusive research topic and ADS uses the data from Lidar and stereo-images to glean the interesting objects [27,28].…”

Section: Perceptionmentioning

confidence: 99%

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

Hussain

Rafique

Kim

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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A frequent cause of auto accidents is disregarding the proximal traffic of an ego-vehicle during lane changing. Presumably, in a split-second-decision situation we may prevent an accident by predicting the intention of a driver before her action onset using the neural signals data, meanwhile building the perception of surroundings of a vehicle using optical sensors. The prediction of an intended action fused with the perception can generate an instantaneous signal that may replenish the driver's ignorance about the surroundings. This study examines electromyography (EMG) signals to predict intention of a driver along perception building stack of an autonomous driving system (ADS) in building an advanced driving assistant system (ADAS). EMG are classified into left-turn and right-turn intended actions and lanes and object detection with camera and Lidar are used to detect vehicles approaching from behind. A warning issued before the action onset, can alert a driver and may save her from a fatal accident. The use of neural signals for intended action prediction is a novel addition to camera, radar and Lidar based ADAS systems. Furthermore, the study demonstrates efficacy of the proposed idea with experiments designed to classify online and offline EMG data in real-world settings with computation time and the latency of communicated warnings.

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A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles

Cited by 17 publications

References 82 publications

Research advanced in object detection of autonomous driving based on deep learning

Research advanced in object detection of autonomous driving based on deep learning

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Artificial Proprioceptive Reflex Warning Using EMG in Advanced Driving Assistance System

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