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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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4 results for “Vehicle-to-Infrastructure”

Federated mmWave Beam Selection Utilizing LIDAR Datacs.IT

Federated LIDAR aided beam selection reduces mmWave beam search overhead.

problem Efficient link configuration in mmWave communication systems with reduced beam search overhead.

method Federated learning of LIDAR data to train a shared neural network for beam selection.

result Proposed method significantly outperforms previous works in performance and complexity.

Federated Learning mmWave Communication Beam Selection LIDAR Vehicle-to-Infrastructure

2021-02-04abs ↗pdf ↗

Learning the Wireless V2I Channels Using Deep Neural Networkseess.SP

For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenge…

2019-07-10abs ↗pdf ↗

VTrackIt: A Synthetic Self-Driving Dataset with Infrastructure and Pooled Vehicle Informationcs.CV

VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.

problem Lack of infrastructure and pooled vehicle info in existing AV datasets.

method Developed VTrackIt, a synthetic dataset with intelligent infrastructure and pooled vehicle info, and introduced InfraGAN for trajectory predictions.

result VTrackIt reduces high-risk edge cases in AV trajectory predictions.

AVs Synthetic Dataset Infrastructure Pooled Vehicle Info Trajectory Prediction

2022-07-15abs ↗pdf ↗

A V2X-based Privacy Preserving Federated Measuring and Learning Systemcs.LG

Proposes a privacy-preserving system for federated learning of road networks.

problem Privacy and security of data shared between vehicles and infrastructure.

method Federated learning over V2V and V2N links, non-IID dataset modeling.

result Improves learning performance and prevents eavesdropping.

federated learning privacy-preserving V2X federated measurement vehicle-to-vehicle

2024-01-24abs ↗pdf ↗