While traditional Convolutional Neural Network (CNN)-based semantic segmentation methods have proven effective, they often encounter significant computational challenges due to the requirement for dense pixel-level predictions, which complicates real-time implementation. To address this, we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving, utilizing the capabilities of Visual Transformers. By leveraging the self-attention mechanism inherent in Visual Transformers, our method enhances global contextual awareness, refining the representation of each pixel in relation to the overall scene. This enhancement is critical for quickly and accurately interpreting the complex elements within driving scenarios—a fundamental need for autonomous vehicles. Our experiments conducted on the DriveSeg autonomous driving dataset indicate that our model surpasses traditional segmentation methods, achieving a significant 4.5% improvement in Mean Intersection over Union (mIoU) while maintaining real-time responsiveness. This paper not only underscores the potential for optimized semantic segmentation but also establishes a promising direction for real-time processing in autonomous navigation systems. Future work will focus on integrating this technique with other perception modules in autonomous driving to further improve the robustness and efficiency of self-driving perception frameworks, thereby opening new pathways for research and practical applications in scenarios requiring rapid and precise decision-making capabilities. Further experimentation and adaptation of this model could lead to broader implications for the fields of machine learning and computer vision, particularly in enhancing the interaction between automated systems and their dynamic environments.

Lightweight modules play a key role in 3D object detection tasks for autonomous driving, which are necessary for the application of 3D object detectors. At present, research still focuses on constructing complex models and calculations to improve the detection precision at the expense of the running rate. However, building a lightweight model to learn the global features from point cloud data for 3D object detection is a significant problem. In this paper, we focus on combining convolutional neural networks with self-attention-based vision transformers to realize lightweight and high-speed computing for 3D object detection. We propose light-weight detection 3D (LWD-3D), which is a point cloud conversion and lightweight vision transformer for autonomous driving. LWD-3D utilizes a one-shot regression framework in 2D space and generates a 3D object bounding box from point cloud data, which provides a new feature representation method based on a vision transformer for 3D detection applications. The results of experiment on the KITTI 3D dataset show that LWD-3D achieves real-time detection (time per image < 20 ms). LWD-3D obtains a mean average precision (mAP) 75% higher than that of another 3D real-time detector with half the number of parameters. Our research extends the application of visual transformers to 3D object detection tasks.