Navigation of Autonomous Vehicles Utilizing Hybrid Sensor Fusion Techniques

Numerous sensor integration frameworks have been presented using multiple setups, sensor combinations, and fusion methodologies. Most studies focused on optimizing accuracy and efficiency, but research is still lacking in incorporating these methods into autonomous vehicles (AVs). Some fusion frameworks might perform exceptionally well in lab environments with abundant computing power; however, their use in integrated edge computing (EC) poses severe challenges due to cost and high computational needs. This study proposes a hybrid sensor fusion technique (HSFT) to enhance autonomous vehicle navigation systems based on deep Q networks (DQN). The approach features the control of flow for camera and LiDAR data and the synthesis of an independent algorithm for object identification based on sensor image data. Noise suppression has been implemented using a Federated Kalman Filter (FKF). Contrast enhancement has been achieved with the help of Homomorphic Filtering (HMF) and adaptive thresholding. Recognition and classification have been performed using the YOLO V7 model. The proposed work is evaluated based on requirements such as speed and accuracy rate.