DEEP LEARNING FOR AUTONOMOUS VEHICLE SCENE UNDERSTANDING

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

The rapid development of autonomous vehicles (AVs) has led to significant advancements in scene understanding, a critical aspect of ensuring safe and efficient navigation. Deep learning (DL) has emerged as a powerful tool for processing and interpreting complex sensory data from various onboard sensors such as cameras, LiDAR, and radar. This paper explores the role of deep learning techniques in enhancing scene understanding for autonomous vehicles, encompassing object detection, semantic segmentation, depth estimation, and behavior prediction. We delve into state-of-the-art deep neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, highlighting their applications in detecting and classifying dynamic and static objects, such as pedestrians, vehicles, traffic signs, and road infrastructure. Additionally, we examine advancements in multi-modal fusion, which combines data from different sensors to improve accuracy and robustness in diverse driving conditions. The abstract also addresses challenges such as domain adaptation, real-time processing, and the need for large-scale annotated datasets. Solutions leveraging transfer learning, synthetic data generation, and active learning are discussed to overcome these limitations. Furthermore, the paper considers the implications of deep learning on the ethical and regulatory aspects of autonomous vehicle deployment, emphasizing the importance of transparency, interpretability, and safety assurance in DL models. This comprehensive review underscores the transformative potential of deep learning in autonomous vehicle scene understanding and outlines future research directions to advance the field.

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Keywords

Autonomous Vehicle Perception, Deep Learning for Scene Understanding, Computer Vision in Self-Driving Cars, Object Detection and Semantic Segmentation Sensor Fusion and Environmental Mapping.