Semantic Segmentation in Autonomous Driving: A Comparative Study
Abstract
Autonomous vehicles require high-accuracy semantic segmentation to understand their environment and make real-time decisions. This paper provides a comparative study of different semantic segmentation algorithms, focusing on CNN-based architectures like U-Net and DeepLabV3+, as well as more recent transformer-based models. We evaluate these algorithms on standard autonomous driving datasets, assessing their performance in terms of segmentation accuracy, speed, and computational efficiency. Our results highlight the trade-offs between different models, offering insights for real-world deployment in self-driving cars.
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