Cooperative Raw Sensor Data Fusion for Ground Truth Generation in Autonomous Driving

Ye, Egon; Spiegel, Philip; Althoff, Matthias

doi:10.1109/itsc45102.2020.9294477

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…The model made a good performance in multi-source point cloud fusion, particularly in the low-density scenarios. Ye et al [ 90 ] utilized a state estimation framework and alignment method to match point cloud data from LiDAR sensors in different mounting positions. The method could represent the road from offline point cloud data accurately.…”

Section: Multi-sensor Data Fusion Methodsmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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Environment perception plays a crucial role in autonomous driving technology. However, various factors such as adverse weather conditions and limitations in sensing equipment contribute to low perception accuracy and a restricted field of view. As a result, intelligent connected vehicles (ICVs) are currently only capable of achieving autonomous driving in specific scenarios. This paper conducts an analysis of the current studies on image or point cloud processing and cooperative perception, and summarizes three key aspects: data pre-processing methods, multi-sensor data fusion methods, and vehicle–infrastructure cooperative perception methods. Data pre-processing methods summarize the processing of point cloud data and image data in snow, rain and fog. Multi-sensor data fusion methods analyze the studies on image fusion, point cloud fusion and image-point cloud fusion. Because communication channel resources are limited, the vehicle–infrastructure cooperative perception methods discuss the fusion and sharing strategies for cooperative perception information to expand the range of perception for ICVs and achieve an optimal distribution of perception information. Finally, according to the analysis of the existing studies, the paper proposes future research directions for cooperative perception in adverse weather conditions.

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Section: Multi-sensor Data Fusion Methodsmentioning

confidence: 99%

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Wang,

Wu,

Liang

et al. 2024

Sensors

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“…However, their approach emphasises data communication to reduce transmission overhead without evaluating the impact on perception quality. In principle, offloading raw sensor data is excellent for perception accuracy [29], but could increase transmission costs [30]. However, offloading compressed data can save network resources, but it might degrade detection quality [31].…”

Section: Perception Using C-v2x Communicationmentioning

confidence: 99%

Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving

Hawlader

Robinet

Frank

2023

2023 18th Wireless on-Demand Network Systems and Services Conference (WONS)

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Environmental perception is a key element of autonomous driving because the information received from the perception module influences core driving decisions. An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency. Major constraints on both computation and power have to be taken into account for real-time perception in autonomous vehicles. Larger object detection models tend to produce the best results, but are also slower at runtime. Since the most accurate detectors cannot run in real-time locally, we investigate the possibility of offloading computation to edge and cloud platforms, which are less resource-constrained. We create a synthetic dataset to train object detection models and evaluate different offloading strategies. Using real hardware and network simulations, we compare different trade-offs between prediction quality and end-to-end delay. Since sending raw frames over the network implies additional transmission delays, we also explore the use of JPEG and H.265 compression at varying qualities and measure their impact on prediction metrics. We show that models with adequate compression can be run in real-time on the cloud while outperforming local detection performance.

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“…Simultaneously, it proves that existing DSRC communication technology can meet the data transmission requirements of point cloud fusion in the region of interest (ROI). Similarly, Ye et al [ 30 ] introduced a state estimation framework for multivehicle LiDAR localization and sensor data fusion that utilized raw sensor data between different vehicles and achieved ground truth generation in an offline manner. At the same time, the registration method better matches point cloud data from diverse views, demonstrating that using perceptual data from multiple perspectives has a better auxiliary effect on cooperative perception, but that this increases the detection time.…”

Section: Cooperative Perception Information Fusionmentioning

confidence: 99%

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

Cui

Zhang

Xiao

et al. 2022

Sensors

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Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

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Cooperative Raw Sensor Data Fusion for Ground Truth Generation in Autonomous Driving

Cited by 11 publications

References 21 publications

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Perception Methods for Adverse Weather Based on Vehicle Infrastructure Cooperation System: A Review

Vehicle-to-Infrastructure Communication for Real-Time Object Detection in Autonomous Driving

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

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