Data Fusion and Optimization Techniques for Enhancing Autonomous Vehicle Performance in Smart Cities
Keywords:
Intelligent transportation systems, Connected and autonomous vehicle, Sustainable city, Smart cityAbstract
The study investigates the use of data fusion and optimization techniques to improve the performance of autonomous driving systems in smart city environments. By integrating data from multiple sensors, including LiDAR, radar, cameras, and sensors, the system enhances its perception and understanding of the environment. Additionally, 5G, LTE-V, and DSRC technologies enable V2X communication, facilitating real-time interaction between vehicles, infrastructure, and other road users. The study employs deep learning and reinforcement learning algorithms for real-time path planning, obstacle detection, and energy efficiency optimization. Simulations conducted in various urban scenarios demonstrate significant improvements in obstacle detection accuracy, traffic safety, and reduced energy consumption through optimized vehicle operations. Furthermore, the system's resilience to communication delays and data loss highlights the robustness of the proposed data fusion and optimization framework in dynamic environments.
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