Hazardous Driving Scenario Identification with Limited Training Samples
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8178-6_16
Reference24 articles.
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2. Ke, R., Cui, Z., Chen, Y., et al.: Lightweight edge intelligence empowered near-crash detection towards real-time vehicle event logging. IEEE Trans. Intell. Veh. 8, 2737–2747 (2023)
3. Yu, R., Ai, H., Gao, Z.: Identifying high risk driving scenarios utilizing a CNN-LSTM analysis approach. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6. IEEE (2020)
4. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);S Yamamoto,2020
5. Gao, Z., Xu, J., Zheng, J.Y., et al.: A lightweight VK-net based on motion profiles for hazardous driving scenario identification. In: 2021 IEEE 23rd International Conference on High Performance Computing & Communications (HPCC), pp. 908–913. IEEE (2021)
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