A Study on Improving the Accuracy of Semantic Segmentation for Autonomous Driving

This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model. Two novel improvements were proposed and implemented in this paper: dynamically adjusting the loss function ratio and integrating an attention mechanism (CBAM). First, the loss function weights were adjusted dynamically. The grid search method is used for deciding the best ratio of 7:3. It gives greater emphasis to the cross-entropy loss, which resulted in better segmentation performance. Second, CBAM was applied at different layers of the 2D encoder. Heatmap analysis revealed that introducing it after the second block of 2D image encoding produced the most effective enhancement of important feature representation. The training epoch was chosen for optimizing the best value by experiments, which improved model convergence and overall accuracy. To evaluate the proposed approach, experiments were conducted based on the SemanticKITTI database. The results showed that the improved model achieved higher segmentation accuracy by 64.31%, improved 11.47% in mIoU compared with the conventional 2DPASS model (baseline: 52.84%). It was more effective at detecting small and distant objects and clearly identifying boundaries between different classes. Issues such as noise and variations in data distribution affected its accuracy, indicating the need for further refinement. Overall, the proposed improvements to the 2DPASS model demonstrated the potential to advance semantic segmentation technology and contributed to a more reliable perception of complex, dynamic environments in autonomous vehicles. Accurate segmentation enhances the vehicle’s ability to distinguish different objects, and this improvement directly supports safer navigation, robust decision-making, and efficient path planning, making it highly applicable to real-world deployment of autonomous systems in urban and highway settings.