F-cooper: feature based cooperative perception for autonomous vehicle edge computing system using 3D point clouds

By Qi Chen. IFIP International Information Security Conference, 2019

512 citations recorded by the source corpus.

Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors. Data sharing between vehicles and/or edge servers is limited by the available network bandwidth and the stringent real-time constraints of autonomous driving applications. To address these issues, we propose a point cloud feature based cooperative perception framework (F-Cooper) for connected autonomous vehicles to achieve a better object detection precision. Not only will feature based data be sufficient for the training process, we also use the features' intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. Our experiment results show that by fusing features, we are able to achieve a better object detection result, around 10% improvement for detection within 20 meters and 30% for further distances, as well as achieve faster edge computing with a low communication delay, requiring 71 milliseconds in certain feature selections. To the best of our knowledge, we are the first to introduce feature-level data fusion to connected autonomous vehicles for the purpose of enhancing object detection and making real-time edge computing on inter-vehicle data feasible for autonomous vehicles.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1a903b17048804e4766fbe795aeba79e2f9bc6b7

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In Automotive Engineering

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Citations in the corpus

V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception (2023, methodology): “, 3D bounding boxes, confidence scores) are shared, then fused to a ‘consensus’ prediction, and (3) Intermediate Fusion [4,27,42,55] where intermediate representations are extracted based on each agent’s observation and then shared with CAVs.”
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer (2022, methodology): “To address these issues, recent studies [39,5,44,4] leverage the advantages of multiple viewpoints of the same scene by investigating Vehicle-to-Vehicle (V2V) collaboration, where visual information (e.”
Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors (2019, methodology): “However, we opt to compare (a) the AP 3D results obtained using a single sensor to the reported results produced by Voxelnet [17]; (b) the AP 3D results using two sensors in the T-junction scenario from Section VI-D to the reported results produced by F-Cooper [20] in a similar scenario.”
CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers (2022, methodology): “We compare with the state-of-the-art multi-agent perception algorithms: F-Cooper [21], AttFuse [12], V2VNet [20], and DiscoNet [14].”
3D Object Detection for Autonomous Driving: A Comprehensive Survey (2022, background): “The fused information can be raw sensory inputs from other agents [34, 367], which cost little communication bandwidth and is quite efficient for detection, and it can also be compressed feature maps [33, 295, 279, 136], which cost non-negligible communication bandwidth but generally lead to better ”