Autonomous driving technology is shifting from traditional modular design to end-to-end design. Modular design includes three main modules: perception, decision planning, and execution control, with each module optimized independently. However, information transfer between modules may result in loss, and it is difficult to handle complex scenarios.
End-to-end design generates control commands directly from raw sensor data, eliminating intermediate steps. Through large-scale data training, it can learn more complex decision rules and better handle various scenarios. However, end-to-end systems have poor interpretability and difficulty in problem localization.
Tesla's FSD V12 adopts an end-to-end design, achieving significant progress in a short time. It uses neural networks to generate control commands directly from visual input, similar to human driving. This approach can learn more flexible driving strategies without being limited by preset rules.
End-to-end design is considered the future direction of autonomous driving. It can fully leverage the advantages of deep learning to achieve more intelligent driving decisions. However, issues such as interpretability and safety still need to be addressed. Future autonomous driving systems may combine the advantages of both modular and end-to-end approaches to achieve more reliable autonomous driving.
Overall, end-to-end design has brought new breakthroughs to autonomous driving but still requires further improvement. Autonomous driving technology is in a rapid development stage, with the potential to achieve higher levels of autonomy in the future.