Nya applikationer för kooperativ perception

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Examensarbete på kandidatnivå
Bachelor Thesis

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As connected and autonomous vehicles become more prevalent, cooperative perception is increasingly vital for ensuring traffic safety and efficiency in urban environments. This project investigates how cooperative perception can enhance intelligent transportation systems by reducing collision risks and improving traffic efficiency through real-time data exchange between vehicles and infrastructure. The system is developed in a simulation framework based on SUMO called ms-van3t. Which also integrates decentralized communication, collision avoidance, and route planning. We introduce a new concept called ITS-fairy, which uses a Server Local Dynamic Map to support real-time data sharing and hazard detection. In addition, a safety mode based on Time-to-Collision and Space-to-Collision estimations provides proactive warnings to prevent crashes. Additionally, a centralized planner is introduced to reduce congestion by assigning routes based on global traffic conditions. The implementations were evaluated through simulations of urban traffic scenarios such as intersections and roundabouts. The results demonstrate ensured safety in urban scenarios and more coordinated traffic behavior under high-density conditions. We conclude that the developed platform offers a robust foundation for continued research and development in cooperative vehicular systems.

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Cooperative Perception, Intelligent Transportation Systems, Autonomous Vehicles, S-LDM, Simulation

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