FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions

2024-01-13 · Lin Lu

Scoring the driving performance of various drivers on a unified scale, based on how safe or economical they drive on their daily trips, is essential for the driver profile task. Connected vehicles provide the opportunity to collect real-world driving data, which is advantageous for constructing scoring models. However, the lack of pre-labeled scores impede the use of supervised regression models and the data privacy issues hinder the way of traditionally data-centralized learning on the cloud side for model training. To address them, an unsupervised scoring method is presented without the need for labels while still preserving fairness and objectiveness compared to subjective scoring strategies. Subsequently, a federated learning framework based on vehicle-cloud collaboration is proposed as a privacy-friendly alternative to centralized learning. This framework includes a consistently federated version of the scoring method to reduce the performance degradation of the global scoring model caused by the statistical heterogeneous challenge of local data. Theoretical and experimental analysis demonstrate that our federated scoring model is consistent with the utility of the centrally learned counterpart and is effective in evaluating driving performance.

📄 PDF Abstract BibTeX arXiv:2401.06953

Code (0)

등록된 구현이 없습니다.

Tasks

Fairness Federated Learning

A Scoring Method for Driving Safety Credit Using Trajectory Data

2018-11-28 · Wenfu Wang, Weijie Yang, An Chen, Zhijie Pan

Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers wit…

Credit score General Classification

A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems

2025-02-17 · Zikang Xiong, Joe Kurian Eappen, Suresh Jagannathan

In autonomous driving systems, motion planning is commonly implemented as a two-stage process: first, a trajectory proposer generates multiple candidate trajectories, then a scoring mechanism selects the most suitable tr…

Autonomous Driving Motion Planning

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation

2025-08-22 · Guangyu Sun, Jingtao Li, Weiming Zhuang, Chen Chen 외 arxiv

Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulations, cloud-based FMs cannot directly acces…

Federated Learning Autonomous Driving Object Detection

EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction

2023-12-15 · Longzhong Lin, Xuewu Lin, Tianwei Lin, Lichao Huang 외

Motion prediction is a crucial task in autonomous driving, and one of its major challenges lands in the multimodality of future behaviors. Many successful works have utilized mixture models which require identification o…

Autonomous Driving motion prediction Prediction regression

Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach

2025-07-08 · Xiaobing Chen, Boyang Zhang, Xiangwei Zhou, Mingxuan Sun 외

The integration of Federated Learning (FL) and Mixture-of-Experts (MoE) presents a compelling pathway for training more powerful, large-scale artificial intelligence models (LAMs) on decentralized data while preserving p…

Edge-computing Federated Learning Mixture-of-Experts