Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey

Bai, Zhengwei; Wu, Guoyuan; Qi, Xuewei; Liu, Yongkang; Oguchi, Kimio; Barth, Matthew

doi:10.1109/iv51971.2022.9827461

Cited by 43 publications

(16 citation statements)

References 58 publications

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“…This approach of autonomous driving, however, faces fundamental limitations in terms of safety and efficiency. As all the sensors are equipped on the vehicle, the perception capability of a self-driving vehicle has a limited range and can be blocked by other objects such as vehicles, buildings and trees [1]. Various sensing technologies, such as camera, ultrasonic sensor, Radar and LiDAR, have been used for self-driving vehicles [20], [21].…”

Section: A Limitations Of Self-driving Technologiesmentioning

confidence: 99%

“…The promising application of roadside infrastructure for autonomous driving has attracted many research interest, where topics like perception [1], edge computing [34], and security [35] have been extensively discussed. In this paper, we study the network communication solutions for IVCAD.…”

Section: Volume mentioning

confidence: 99%

“…Roadside infrastructure can support autonomous driving by improving the sensing capability and robustness better than the on-board perception equipment [1], and can coordinate the driving decision among vehicles to improve the traffic efficiency [2]. Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is drawing a lot of research interest recently [3].…”

Section: Introductionmentioning

confidence: 99%

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On Scalable Network Communication for Infrastructure-Vehicle Collaborative Autonomous Driving

Yang

Yin

Yeung

et al. 2023

IEEE Open J. Veh. Technol.

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Emerging topics such as environmental protection and energy utilization have pushed research and development of electric vehicles and hybrid electric vehicles (EVs/HEVs). In the last few decades, numerous technologies have been developed for EV/HEV importance. In this article, key research topics in the area of EVs/HEVs, namely electric machines, electrochemical energy sources, wireless charging infrastructure, and latest EV/HEV models are covered. This paper aims to consolidate the key emerging technologies in this field and provide the readers a blueprint to begin their own journeys.INDEX TERMS Electric machine, induction machine, wound field synchronous machine, reluctance machine, stator permanent-magnet machine, field modulation machine, high-speed machine, electrochemical energy source, battery, fuel cell, ultracapacitor, wireless charging infrastructure, part-and-charge, coil design, move-and-charge, electric vehicle, hybrid vehicle.

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Section: A Limitations Of Self-driving Technologiesmentioning

confidence: 99%

Section: Volume mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On Scalable Network Communication for Infrastructure-Vehicle Collaborative Autonomous Driving

Yang

Yin

Yeung

et al. 2023

IEEE Open J. Veh. Technol.

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“…Moreover, the roadside object scale varies violently due to the different heights of the visual sensors installed. Finally, the information sensed by the roadside perception system needs to be transmitted to the vehicle for decision and control via wireless communication technology [ 9 ], which has high requirements for real-time and efficient deployment of detection algorithms. Hence, designing an effective and efficient algorithm for roadside object detection is a pressing problem.…”

Section: Introductionmentioning

confidence: 99%

RD-YOLO: An Effective and Efficient Object Detector for Roadside Perception System

Huang

2022

Sensors

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In recent years, intelligent driving technology based on vehicle–road cooperation has gradually become a research hotspot in the field of intelligent transportation. There are many studies regarding vehicle perception, but fewer studies regarding roadside perception. As sensors are installed at different heights, the roadside object scale varies violently, which burdens the optimization of networks. Moreover, there is a large amount of overlapping and occlusion in complex road environments, which leads to a great challenge of object distinction. To solve the two problems raised above, we propose RD-YOLO. Based on YOLOv5s, we reconstructed the feature fusion layer to increase effective feature extraction and improve the detection capability of small targets. Then, we replaced the original pyramid network with a generalized feature pyramid network (GFPN) to improve the adaptability of the network to different scale features. We also integrated a coordinate attention (CA) mechanism to find attention regions in scenarios with dense objects. Finally, we replaced the original Loss with Focal-EIOU Loss to improve the speed of the bounding box regression and the positioning accuracy of the anchor box. Compared to the YOLOv5s, the RD-YOLO improves the mean average precision (mAP) by 5.5% on the Rope3D dataset and 2.9% on the UA-DETRAC dataset. Meanwhile, by modifying the feature fusion layer, the weight of RD-YOLO is decreased by 55.9% while the detection speed is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 71.9 frames/s (FPS) and achieves higher accuracy than the previous approaches with a similar FPS.

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“…At present, there are some research reviews on object detection based on onboard LiDAR, as shown in [ 21 , 22 , 23 ], but there are few reviews on roadside LiDAR perception. Recently, Bait et al [ 24 ] reviewed object detection and tracking based on roadside sensors. However, the detection methods and datasets related to roadside LiDAR are less involved.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

Sun

Wang

et al. 2022

Sensors

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Light Detection and Ranging (LiDAR) technology has the advantages of high detection accuracy, a wide range of perception, and not being affected by light. The 3D LiDAR is placed at the commanding height of the traffic scene, the overall situation can be grasped from the perspective of top view, and the trajectory of each object in the traffic scene can be accurately perceived in real time, and then the object information can be distributed to the surrounding vehicles or other roadside LiDAR through advanced wireless communication equipment, which can significantly improve the local perception ability of an autonomous vehicle. This paper first describes the characteristics of roadside LiDAR and the challenges of object detection and then reviews in detail the current methods of object detection based on a single roadside LiDAR and multi-LiDAR cooperatives. Then, some studies for roadside LiDAR perception in adverse weather and datasets released in recent years are introduced. Finally, some current open challenges and future works for roadside LiDAR perception are discussed. To the best of our knowledge, this is the first work to systematically study roadside LiDAR perception methods and datasets. It has an important guiding role in further promoting the research of roadside LiDAR perception for practical applications.

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Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey

Cited by 43 publications

References 58 publications

On Scalable Network Communication for Infrastructure-Vehicle Collaborative Autonomous Driving

On Scalable Network Communication for Infrastructure-Vehicle Collaborative Autonomous Driving

RD-YOLO: An Effective and Efficient Object Detector for Roadside Perception System

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

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