References
- J. Mao, S. Shi, X. Wang, and H. Li,”3D Object Detection for Autonomous Driving: A Comprehensive Survey,” International Journal of Computer Vision, pp. 1–55, 2023.
- Z. Wei, et al., “Mmwave Radar And Vision Fusion For Object Detection In Autonomous Driving: A Review,” Sensors, vol. 22, p. 2542, 2022.
- B. Pan, L. Zhang, and H. Wang, “Multi-Stage Feature Pyramid Stereo Network-Based Disparity Estimation Approach For Two To ThreeDimensional Video Conversion,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, pp. 1862–1875, 2020.
- Y. Zhang, et al., ”PMPF: Point-Cloud MultiplePixel Fusion-Based 3D Object Detection for Autonomous Driving,” Remote Sensing, vol. 15, p. 1580, 2023.
- D. Feng, et al., ”Deep Multi-Modal Object Detection And Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, pp. 1341–1360, 2020.
- S. Yao, et al., ”Radar-Camera Fusion For Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review,” arXiv preprint arXiv:2304.10410, 2023.
- A. Hazarika, et al., ”Multi-camera 3D Object Detection for Autonomous Driving Using Deep Learning and Self-Attention Mechanism,” IEEE Access, 2023.
- S. Rinchen, B. Vaidya, and H. T. Mouftah, ”Scalable Multi-Task Learning R-CNN for Object Detection in Autonomous Driving.” In 2023 International Wireless Communications and Mobile Computing (IWCMC), Marrakesh, 19–23 June 2023; pp. 518–523.
- S. A. Khan, H. J. Lee, and H. Lim, ”Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach,” Electronics, vol. 12, p. 2768, 2023.
- S. Liang, et al., ”Edge YOLO: Real-Time Intelligent Object Detection System Based on EdgeCloud Cooperation in Autonomous Vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, pp. 25345–25360, 2022.
- S.-W. Kim, et al., ”Edge-Network-Assisted Real-Time Object Detection Framework for Autonomous Driving,” IEEE Network, vol. 35, pp. 177–183, 2021.
- Y. Wu, et al., ”An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving,” Applied Sciences, vol. 9, p. 4363, 2019.
- Y. Zhou, et al., ”Object Detection in Autonomous Driving Scenarios Based OnAn Improved Faster-RCNN,” Applied Sciences, vol. 11, p. 11630, 2021.
- Y. Cao, C. Li, Y. Peng, and H. Ru,”MCSYOLO: A Multiscale Object Detection Method for Autonomous Driving Road Environment Recognition,” IEEE Access, vol. 11, pp. 22342–22354, 2023.
- W. Jang, et al., ”R-TOD: Real-time Object Detector With Minimized End-To-End Delay for Autonomous Driving,” in 2020 IEEE Real-Time Systems Symposium (RTSS), 1–4th December 2020, pp. 191–204.
- S. Wu, Y. Yan, and W. Wang, ”CF-YOLOX: An Autonomous Driving Detection Model for MultiScale Object Detection,” Sensors, vol. 23, p. 3794, 2023.
- Z. Yuan, et al., ”Temporal-Channel Transformer for 3D Lidar-Based Video Object Detection for Autonomous Driving,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, pp. 2068–2078, 2021.
- K. Geng, G. Dong, and W. Huang, ”Robust Dual-Modal Image Quality Assessment Aware Deep Learning Network for Traffic Targets Detection Of Autonomous Vehicles,” Multimedia Tools and Applications, vol. 81, pp. 6801–6826, 2022.
- L.-H. Wen and K.-H. Jo, ”Fast and Accurate 3D Object Detection For Lidar-CameraBased Autonomous Vehicles Using One Shared VoxelBased Backbone,” IEEE Access, vol. 9, pp. 22080–22089, 2021.
- K. Roszyk, M. R. Nowicki, and P. Skrzypczynski, ”Adopting the Yolov4 Architecture for Low-Latency Multispectral Pedestrian Detection in Autonomous Driving,” Sensors, vol. 22, p. 1082, 2022.
- Y. Shao, et al., ”PV-SSD: A Projection and Voxelbased Double Branch Single-Stage 3D Object Detector,” arXiv preprint arXiv:2308.06791, 2023.
- Y Cai, et al., ”YOLOv4-5D: An Effective And Efficient Object Detector for Autonomous Driving,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–13, 2021.
- H. Nguyen, ”Improving Faster R-CNN Framework for Fast Vehicle Detection,” Mathematical Problems in Engineering, vol. 2019, pp. 1–11, 2019.
- Q. Tang, G. Cao, and K.-H. Jo, ”Integrated Feature Pyramid Network with Feature Aggregation for Traffic Sign Detection,” IEEE Access, vol. 9, pp. 117784–117794, 2021.
- N. Zaghari, et al., ”The Improvement in Obstacle Detection in Autonomous Vehicles Using YOLO Non-Maximum Suppression Fuzzy Algorithm,” The Journal of Supercomputing, vol. 77, pp. 13421–13446, 2021.
- V. John and S. Mita, ”Deep feature-level Sensor Fusion Using Skip Connections for Real-Time Object Detection in Autonomous Driving,” Electronics, vol. 10, p. 424, 2021.
