Automotive
Technical Paper

DriVE-Net: A Multi-Objective Framework for Generating Diverse, Realistic, and Hazard-Aware Driving Scenarios

2025-99-0133

11/11/2025

Features
Event
2025 International Conference on Big Data, Internet of Things and Intelligent Transportation (BDIT2025)
Authors
Dongxuan Xie School of Computer Science and Engineering, Changchun Univer
Dongyang Li School of Computer Science and Engineering, Changchun Univer
Youkang Zhang School of Computer Science and Engineering, Changchun Univer
Yingjie Zhao Northeast Industries Group Co., Ltd., Changchun, China
Baofeng Hong Northeast Industries Group Co., Ltd., Changchun, China
Nan Wang Key Laboratory of CNC Equipment Reliability, Ministry of Edu
Abstract
Content
.
Meta Tags
Topics
Safety critical systems
Machine learning
Logistics
Risk assessments
Research and development
Education and training
Automated driving systems
Data exchange
Affiliated or Co-Author
School of Computer Science and Engineering, Changchun Univer
Northeast Industries Group Co., Ltd., Changchun, China
Key Laboratory of CNC Equipment Reliability, Ministry of Edu
Details
DOI
https://doi.org/10.4271/2025-99-0133
Citation
Xie, D., Li, D., Zhang, Y., Zhao, Y., et al., "DriVE-Net: A Multi-Objective Framework for Generating Diverse, Realistic, and Hazard-Aware Driving Scenarios," 2025 International Conference on Big Data, Internet of Things and Intelligent Transportation (BDIT2025), Zhengzhou, China, July 19, 2025, https://doi.org/10.4271/2025-99-0133.
Additional Details
Publisher
SAE International
Published
11/11/2025
Product Code
2025-99-0133
Content Type
Technical Paper
Language
English