Autonomous driving maps are essential for the safe operation of intelligent vehicles, especially on Chinese urban roads, where road structures are complex and traffic conditions change frequently. For a long time, map production has relied on specialized surveying and extensive manual annotation, resulting in high costs, low efficiency, and long update cycles, making large-scale coverage and continuous updating difficult to sustain. Under these conditions, end-to-end autonomous driving map generation has gradually become an important direction in this field. Its fundamental goal is to infer structured map outputs more directly from sensor observations, thereby reducing dependence on complex intermediate procedures and intensive manual intervention. With the continued development of deep learning, the two core tasks of autonomous driving map generation—lane network generation and lane topology prediction—have both shifted toward end-to-end approaches. Concurrently, industrial end-to-end systems have been developed for city-scale map production and updating, while large models, vision-language models, and agent-based systems have further expanded the research space in this area. Nevertheless, several challenges remain unresolved, including generalization in complex scenes, dynamic change detection, interpretability, and consistency in multi-vehicle collaboration. Therefore, it is important to systematically review existing research to provide clearer guidance for the future development of this field.

For lane network generation, the technical route has evolved from early convolutional neural network (CNN)-based methods to Transformer-based and hybrid architectures. CNN-based methods were widely adopted at an early stage due to their mature operators, stable training, and low deployment cost. They are effective in modeling local geometry; however, their ability to preserve long-range structural consistency becomes more limited in complex scenes. Transformer-based methods later emerged as a major direction because query-based decoding is well suited for structured instance prediction and global relation modeling. Subsequent studies further expanded this line of work to include geometric constraints, map element representation, prior-guided prediction, and temporal consistency in online mapping. Hybrid architectures combine convolutional feature extraction, Transformer-based reasoning, graph modules, and temporal memory, enabling local geometric precision, topological consistency, and engineering feasibility to be addressed within a unified framework. A similar shift can be observed in lane topology prediction. Early methods mainly relied on local connection inference, whereas later studies increasingly treated topology as a structured prediction problem involving order, connectivity, and global consistency. Transformer-based methods strengthened long-range dependency modeling and gradually incorporated geometry, order, connectivity, and generation into a more unified framework. In contrast, graph neural network-based methods explicitly represent node relationships, edge constraints, and multi-scale connectivity patterns through graph structure. Hybrid methods further combine segmentation, sequence modeling, graph reasoning, and redundant supervision to improve robustness in complex road scenes. The main challenge is no longer limited to recovering local geometric shapes, but increasingly lies in maintaining structural consistency when map elements, topological relations, temporal information, and prior knowledge are considered together. Beyond these academic methods, industrial practice has also developed end-to-end map generation systems for large-scale urban deployment, aiming to improve automation, reduce production costs, and shorten update latency. Related studies also cover online high-definition map construction and pseudo-label learning under weak or missing annotations. Meanwhile, large models and vision-language models have gradually entered this line of research. Their potential in map generation has attracted increasing attention; however, their practical use is still constrained by data quality, structural priors, engineering controllability, and the requirements of real production environments.

Overall, end-to-end autonomous driving map generation is reshaping the conventional map construction paradigm and has shown clear advantages in process simplification, timeliness, and structured prediction. With the introduction of large models, vision-language models, and agent-based systems, map generation may gradually move beyond geometric construction toward a stage that also involves semantic understanding and human–machine interaction. Further progress in this direction will depend not only on model capability but also on the establishment of stable data pipelines, quality control mechanisms, and closed-loop engineering workflows, thereby supporting the large-scale and stable deployment of autonomous driving systems.