A novel hybrid deep learning approach for 3D object detection and tracking in autonomous driving

Recently Object detection and tracking using fusion of LiDAR and RGB camera for the autonomous vehicle environment is a challenging task. The existing works initiates several object detection and tracking frameworks using Artificial Intelligence (AI) algorithms. However, they were limited with high false positives and computation time issues thus lacking the performance of autonomous driving environment. The existing issues are resolved by proposing Hybrid Deep Learning based Multi Object Detection and Tracking (HDL-MODT) using sensor fusion methods. The proposed work performs fusion of solid state LiDAR, Pseudo LiDAR, and RGB camera for improving detection and tracking quality. At first, the multi-stage preprocessing is done in which noise removal is performed using Adaptive Fuzzy Filter (A-Fuzzy). The pre-processed fused image is then provided for instance segmentation to reduce the classification and tracking complexity. For that, the proposed work adopts Lightweight General Adversarial Networks (LGAN). The segmented image is provided for object detection and tracking using HDL. For reducing the complexity, the proposed work utilized VGG-16 for feature extraction which forms the feature vectors. The features vectors are then provided for object detection using YOLOv4. Finally, the detected objects were tracked using Improved Unscented Kalman Filter (IUKF) and mapping the vehicles using time based mapping by considering their RFID, velocity, location, dimension and unique ID. The simulation of the proposed work is carried out using MATLAB R2020a simulation tool and performance of the proposed work is compared with several metrics that show that the proposed work outperforms than the existing works.

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Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).