ENHANCING DEEP LEARNING MODELS FOR REAL-TIME DECISION MAKING IN AUTONOMOUS SYSTEMS
Keywords:
deep learning, real-time decision making, autonomous systems, reinforcement learning, sensor data fusion, robotics, model compressionAbstract
Real-time decision-making in autonomous systems, such as self-driving cars, drones, and robotics, requires deep learning models that are both accurate and capable of making rapid, reliable decisions in dynamic environments. This paper explores strategies for enhancing deep learning models to improve their efficiency and adaptability for real-time applications. Techniques like model compression, transfer learning, and reinforcement learning are discussed as methods to reduce computational overhead while maintaining high performance. The integration of sensor data fusion, predictive algorithms, and multi-agent systems is also explored to optimize real-time decision-making capabilities. The study examines existing challenges such as latency, safety, and robustness, and proposes solutions for overcoming these obstacles in autonomous system deployment. Case studies from autonomous vehicles and robotics demonstrate the effectiveness of these enhanced models in various real-world scenarios.
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