Neural Computing for Next-Gen Autonomous Systems

Over the last few years, the increasing number of autonomous systems has required stronger, smarter, and more context-aware frameworks that can work effectively in uncertain and dynamic settings. This research proposes a multisensor fusion architecture based on deep learning for autonomous control and navigation systems. The method combines complementary sensor inputs like LiDAR, radar, and vision data using a layered pipeline, including data acquisition, preprocessing, fusion, and neural decision-making. The combination strategy improves environmental perception, and real-time object detection, path planning, and decision output are accomplished using convolutional and recurrent neural networks. Experiment results indicate significant system accuracy, response time, and adaptability improvements compared with the conventional modular control structures. The proposed approach also demonstrates enhanced capabilities in more complex environments that require adequate obstacle perception and real-time path search. Despite the computational difficulties due to readout characteristics and issues of interpretability, the results show encouraging evidence towards the scalable and safe deployment of autonomous systems. Future development and the development of lighter AI models, explainable decision mechanisms, and approaches for domain adaptation are described in the conclusion of this work.