An Obstacle Detection Method Based on Longitudinal Active Vision
Author:
Affiliation:
1. School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
2. Qingte Group Co., Ltd., Qingdao 266106, China
Abstract
Funder
Shandong Province Youth Innovation Team Plan for Universities
Natural Science Foundation of Shandong Province
Publisher
MDPI AG
Link
https://www.mdpi.com/1424-8220/24/13/4407/pdf
Reference29 articles.
1. World Health Organization (2023). Global Status Report on Road Safety 2023, World Health Organization.
2. Ci, W., Xu, T., Lin, R., Lu, S., Wu, X., and Xuan, J. (2023). A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud. Remote Sens., 15.
3. Badrloo, S., Varshosaz, M., Pirasteh, S., and Li, J. (2022). Image-Based Obstacle Detection Methods for the Safe Navigation of Unmanned Vehicles: A Review. Remote Sens., 14.
4. Convex vision-based negative obstacle detection framework for autonomous vehicles;Dodge;IEEE Trans. Intell. Veh.,2022
5. Ding, N., Mokhtarzadeh, A.A., and Geng, J. (2023, January 21–22). A Multi-lidar Based Dynamic Obstacle Detection and Prediction Method. Proceedings of the 2023 International Conference on the Cognitive Computing and Complex Data (ICCD), IEEE2023, Huaian, China.
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