On the public citation graph

https://doi.org/10.2139/ssrn.4973353

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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-34372-9openalex-extracted
Computer Vision
inferredcites →https://doi.org/10.1007/978-3-031-19842-7_7openalex-extracted
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer
inferredcites →https://doi.org/10.1007/978-3-031-20077-9_1openalex-extracted
BEVFormer: Learning Bird’s-Eye-View Representation from Multi-camera Images via Spatiotemporal Transformers
inferredcites →https://doi.org/10.1016/j.aap.2023.106965openalex-extracted
Physics-informed multi-step real-time conflict-based vehicle safety prediction
inferredcites →https://doi.org/10.1016/j.trc.2021.103309openalex-extracted
Trajectory planning for connected and automated vehicles at isolated signalized intersections under mixed traffic environment
inferredcites →https://doi.org/10.1016/j.trc.2022.103982openalex-extracted
Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning
inferredcites →https://doi.org/10.1016/j.trc.2024.104750openalex-extracted
Data poisoning attacks in intelligent transportation systems: A survey
inferredcites →https://doi.org/10.1016/j.trc.2024.104860openalex-extracted
A cooperative perception based adaptive signal control under early deployment of connected and automated vehicles
inferredcites →https://doi.org/10.1016/j.tre.2024.103497openalex-extracted
Bi-scale car-following model calibration based on corridor-level trajectory
inferredcites →https://doi.org/10.1023/b:visi.0000045324.43199.43openalex-extracted
Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods
inferredcites →https://doi.org/10.1109/cvpr.2017.291openalex-extracted
Optical Flow Estimation Using a Spatial Pyramid Network
inferredcites →https://doi.org/10.1109/cvpr52729.2023.01318openalex-extracted
V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception
inferredcites →https://doi.org/10.1109/icivc.2018.8492786openalex-extracted
Moving Target Detection and Tracking Based on Pyramid Lucas-Kanade Optical Flow
inferredcites →https://doi.org/10.1109/itsc57777.2023.10422029openalex-extracted
A Roadside Cooperative Perception System with Multi-Camera Fusion at an Intersection
inferredcites →https://doi.org/10.1109/iv55156.2024.10588673openalex-extracted
Deception for Advantage in Connected and Automated Vehicle Decision-Making Games*
inferredcites →https://doi.org/10.1109/iv55156.2024.10588684openalex-extracted
V2X-DSI: A Density-Sensitive Infrastructure LiDAR Benchmark for Economic Vehicle-to-Everything Cooperative Perception
inferredcites →https://doi.org/10.1109/ivs.2016.7535520openalex-extracted
Cooperative positioning and radar sensor fusion for relative localization of vehicles
inferredcites →https://doi.org/10.1109/jsac.2022.3221995openalex-extracted
VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction
inferredcites →https://doi.org/10.1109/tcomm.2021.3126650openalex-extracted
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning
inferredcites →https://doi.org/10.1109/tits.2020.3028424openalex-extracted
Cooperative Perception for 3D Object Detection in Driving Scenarios Using Infrastructure Sensors
inferredcites →https://doi.org/10.1109/tits.2022.3153815openalex-extracted
Survey on Cooperative Perception in an Automotive Context
inferredcites →https://doi.org/10.1109/tits.2023.3304607openalex-extracted
Automated Vehicle Identification Based on Car-Following Data With Machine Learning
inferredcites →https://doi.org/10.1109/tiv.2023.3282567openalex-extracted
HYDRO-3D: Hybrid Object Detection and Tracking for Cooperative Perception Using 3D LiDAR
inferredcites →https://doi.org/10.1109/tmc.2022.3150401openalex-extracted
WAVE: Edge-Device Cooperated Real-Time Object Detection for Open-Air Applications
inferredcites →https://doi.org/10.1109/tmc.2023.3343709openalex-extracted
Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles
inferredcites →https://doi.org/10.1109/tvt.2021.3066210openalex-extracted
Active Learning With Noisy Labelers for Improving Classification Accuracy of Connected Vehicles
inferredcites →https://doi.org/10.1109/wacv56688.2023.00124openalex-extracted
Adaptive Feature Fusion for Cooperative Perception using LiDAR Point Clouds
inferredcites →https://doi.org/10.1145/3447993.3483242openalex-extracted
EMP
inferredcites →https://doi.org/10.1177/1555343417695197openalex-extracted
Autonomous Driving Systems: A Preliminary Naturalistic Study of the Tesla Model S
inferredcites →https://doi.org/10.14711/thesis-991013222957003412openalex-extracted
On the robotic uncertainty of fully autonomous traffic
inferredcites →https://doi.org/10.22028/d291-26234openalex-extracted
Lucas/Kanade meets Horn/Schunck : combining local and global optic flow methods
inferredcites →https://doi.org/10.3390/s22155535openalex-extracted
Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review
inferredcites →https://doi.org/10.48550/arxiv.2003.10152openalex-extracted
SOLOv2: Dynamic and Fast Instance Segmentation
inferredcites →https://doi.org/10.48550/arxiv.2112.14947openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2208.00116openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2303.07601openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2310.00400openalex-extracted

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1080/15472450.2025.2512336openalex-snapshot
Perceived risk quantification for automated vehicles: a method with unified metrics
inferred← citeshttps://doi.org/10.1109/iv64158.2025.11097652openalex-snapshot
Optimized Cooperative Car-Following Through Lightweight Vehicle-to-Vehicle Intent Sharing
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