muscle-fibers
发表于 2025-3-30 09:08:38
ITS Traffic Management with Connected Vehicles: An Overview, efficiency could be improved. However, the impact of CV penetration rate and robustness of CV-based ITS solutions remain to be studied. With the comprehensive reviews on studies of CV-based ITS traffic management, this chapter can help guide future traffic management policy design with incorporation of data-based solutions.
Enervate
发表于 2025-3-30 14:03:08
Enabling Reconfigurable Intelligent Surface for V2X Communication Systems,e review of RIS-assisted communication systems. Then, modeling of the combination of V2X and RIS, including RIS-assisted vehicular communication systems and RIS-assisted vehicular edge computing is presented. At last, we also discuss the future development trend and challenge of RIS-assisted V2X communication systems.
Conspiracy
发表于 2025-3-30 17:21:44
Real-Time Object Detection for ITS Applications,on object detection algorithms. Moreover, this chapter introduces lightweight methods that focus on reducing the consumption of inference. The knowledge distillation algorithm is described in detail and applied to the YOLOv5 model. Finally, this chapter describes the YOLOv5 knowledge distillation experiment and analyzes the experimental results.
肉身
发表于 2025-3-31 00:26:58
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技术
发表于 2025-3-31 01:00:54
Multi-sensor Fusion for Perception in Complex Traffic Environments,d feature-level fusion. Furthermore, this chapter will give some recommendations on the forward research directions to address the problems that have been encountered in the field of multi-sensor fusion.
陈腐的人
发表于 2025-3-31 05:26:55
A Cooperative Positioning Enhancement for Blockchain-Enabled Vehicular Networks, a blockchain model based on consensus algorithm is further introduced to share the positioning error information. Additionally, the method‘s performance and effectiveness in terms of accuracy are verified in different scenarios.
多山
发表于 2025-3-31 11:51:47
Cooperative Cloud-Edge Computing for Integrated Sensing and Communication in Internet of Vehicles,matical for the joint communication and computation resource allocation problem and solves it with a deep reinforcement learning-based solution. Moreover, this chapter discusses future research directions.