Cooperative Perception for Modern Transportation

Unmanned aerial vehicles (UAVs) and unmanned ground vehicles will become more and more widely used in the field of transportation. Collaborative perception technology will play a key role in this process. Collaborative perception is a way to use the collaboration between various sensors and infrastructure to jointly perceive the environment. Collaborative perception can improve the perception accuracy and coverage, reduce the perception cost, and support a variety of intelligent transportation types, intelligent security, and other applications. Common sensing technologies in collaborative sensing include radar, camera, lidar, ultrasonic sensor, and inertial measurement units. The choice of sensing technology mainly depends on factors such as sensing demand, cost, and environmental conditions. The perception data in collaborative perception is usually multi-source, heterogeneous, and needs fusion processing to improve the perception accuracy and robustness.

This Special Issue aims to explore the modeling theories and methods for UAV and self-driving vehicles (SDVs) in intelligent transportation systems. The application of drones in modern transportation systems will bring more opportunities and challenges. The efficiency of traffic management can be greatly improved by leveraging the high-altitude monitoring and rapid response capabilities of drones, combined with the big data analysis and real-time monitoring technology of intelligent transportation systems. At the same time, it can also be applied to the field of public safety and logistics distribution, improving the overall benefit to society and human quality of life. Collaborative perception processes and integrates data collected by multiple collaborative sensors to produce more accurate and complete perceptual results. Collaborative perception solves the two main problems of remote occlusion and sparse data in single perception. Collaborative perception usually involves algorithms and technologies such as data processing, distributed computing, and artificial intelligence. These greatly improve the ability of the drones to integrate information with the existing sensors in the intelligent transportation system.

Topics including, but are not limited to, the following:

• Fusion perception of visual, infrared, laser, and other sensors.
• Intelligent video surveillance in modern transportation systems.
• Remote sensing and data analysis for modern transportation.
• Localization and high-definition map for transportation.
• Deep-reinforcement learning for multi-unmanned traffic systems
• Application of artificial intelligence for unmanned aerial vehicles (UAVs) and self-driving vehicles (SDVs);
• Modeling, simulation, and dynamic analysis of the collaboration system for UAVs and SDVs;
• UAV and SDV decision making in a complex urban traffic environment;
• Parameter identification and state estimation of UAVs and SDVs;
• Trajectory prediction and its application to intelligent transportation systems;
• Design of new sensors and novel estimation and data fusion algorithms for UAVs and SDVs.
• Dynamic vision sensor and its application in intelligent transportation.
• Spiking Neural Network and neuromorphic computing.

Dr. Hailong Shi
Dr. Sheng Bao
Dr. Jinsheng Xiao
Dr. Jian Zhou
Guest Editors

Manuscript Submission Information

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• Fusion perception
• Computer vision
• Intelligent video surveillance
• Modern transportation systems
• Remote sensing
• High definition map
• Deep learning
• Deep reinforcement learning
• Spiking Neural Network
• Multi-unmanned systems
• Unmanned aerial vehicles
• Self-driving vehicles
• Dynamic vision sensor