In recent years, breakthroughs have been made in the application of artificial intelligence in the field of autonomous driving. From the traditional "perception prediction planning control" discrete architecture, to the current end-to-end big models that are disrupting the entire industry's technological roadmap.
The traditional auto drive system relies on multiple independent modules: target detection, semantic segmentation, path planning, motion control, etc. Each module needs to be trained and optimized separately, and the accumulation of errors between modules becomes a bottleneck in the system. The end-to-end large model achieves deep integration of perception and decision-making by directly mapping multi-sensor data such as cameras and LiDAR into driving instructions.
Since 2026, multiple companies have launched autonomous driving solutions based on visual language models. These models can not only understand semantic information in traffic scenarios, but also perform driving operations based on natural language instructions. For example, when a passenger says' Turn right at the next intersection and stop at the gas station ', the system can accurately understand and plan the corresponding route.
At the perception level, multimodal large models integrate various sensor data such as vision, LiDAR, and millimeter wave radar, greatly improving perception accuracy in complex scenarios such as severe weather and nighttime. At the decision-making level, decision models based on reinforcement learning and imitation learning can make human like decisions in complex traffic environments.
At the same time, the concept of world models has also been introduced into the field of autonomous driving. This type of model can predict the motion trajectories of traffic participants in the next few seconds, providing forward-looking basis for decision-making. Tesla, Waymo, and other companies are actively exploring this direction.
The end-to-end big model has also brought about a paradigm shift in data-driven development. Through the training of massive amounts of real driving data and simulation data, the model can handle boundary situations that are difficult for traditional rule engines to cover, such as long tail scenes and rare road conditions.
Looking ahead, autonomous driving will gradually move from L2+assisted driving to L4 level highly automated driving. The end-to-end big model will play a key role in this process, driving the evolution of cars from transportation to intelligent mobile spaces.