mlsanityData to 14 Oct 2026

E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction

arXiv 2506.04654 ↗First posted 5 Jun 2025cs.AIcited by 0

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Abstract (arXiv)

E-bikes have rapidly gained popularity as a sustainable form of urban mobility, yet their safety implications remain underexplored. This paper analyzes injury incidents involving e-bikes and traditional bicycles using two sources of data, the CPSRMS (Consumer Product Safety Risk Management System Information Security Review Report) and NEISS (National Electronic Injury Surveillance System) datasets. We propose a standardized classification framework to identify and quantify injury causes and severity. By integrating incident narratives with demographic attributes, we reveal key differences in mechanical failure modes, injury severity patterns, and affected user groups. While both modes share common causes, such as loss of control and pedal malfunctions, e-bikes present distinct risks, including battery-related fires and brake failures. These findings highlight the need for tailored safety interventions and infrastructure design to support the safe integration of micromobility devices into urban transportation networks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 5 Jun 2025.

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References · 14

  1. 2023Large Language Modelscited by 780
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  3. 2024Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitatecited by 47
  4. 2025Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoningcited by 29
  5. 2024HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Modelscited by 15
  6. 2025Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learningcited by 11
  7. 2025Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healingcited by 10
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  9. 2025SAKE: Structured Agentic Knowledge Extrapolation for Complex LLM Reasoning via Reinforcement Learningcited by 9
  10. 2025The Application of Large Language Models in Recommendation Systemscited by 8
  11. 2025Enhancing User Intent for Recommendation Systems via Large Language Modelscited by 7
  12. 2024MalleTrain: Deep Neural Network Training on Unfillable Supercomputer Nodescited by 5
2 more
  1. 2025Research on Cloud Platform Network Traffic Monitoring and Anomaly Detection System based on Large Language Modelscited by 5
  2. 2025A Hybrid Attention Framework for Fake News Detection with Large Language Modelscited by 3

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