Skip-SegFormer Efficient Semantic Segmentation for urban driving

Environmental perception is a crucial aspect within the field of autonomous urban driving that provides information about the environment, identifying clear driving areas and possible surrounding obstacles. Semantic segmentation is a widely used perception method for self-driving cars. The predicted image pixels can be used to bias the vehicle's behaviour and avoid collisions. In this work a Semantic Segmentation model based on an architecture called SegFormer is proposed, made more efficient by using what our Skip-Decoder module. The model is fine-tuned on urban driving datasets and produces accurate segmentation masks in a short time, making the architecture perfectly adaptable to an autonomous driving car system.