A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion

A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion

To address poor real-time performance and low accuracy in car-following risk identification, a model based on autoencoders is proposed. Using the SHRP2 natural driving dataset, this paper constructs a car-following risk identification model in two stages. In Stage 1, a deep feedforward neural networ...

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Bibliographic Details
Main Authors: Shuwei Guo, Yunyu Bo, Jie Chen, Yanan Liu, Jiajia Chen, Huimin Ge
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Systems
Subjects:
traffic engineering
risk identification
autoencoder
car-following scenarios
natural driving
Online Access:https://www.mdpi.com/2079-8954/13/1/41
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