Road detection in autonomous vehicles through deep learning

In a rapidly advancing technological landscape, integrating deep learning in autonomous vehicles presents a pivotal challenge and opportunity. Our study briefly introduces some concepts in machine learning and its network structures. We aim to perform road segmentation using a U-Net image segmentation model trained on the Lyft Udacity dataset. We trained the model under different settings by adjusting parameters such as the initial channel, pooling method, activation function, and batch size. The performance of models is evaluated using the IoU metric. By comparing the performance of the models, we identify which settings yield higher performance for road segmentation task. Additionally, we observe the validation loss per epoch to determine which configuration provided the best training stability. This comprehensive evaluation allows us to identify optimal model setup for robust and reliable road segmentation models.