Kang Mou

An In-Depth Analysis of How Autonomous Driving Simulation Solves the Challenges of Unstructured Road Testing! Hongke’s aiSim solution integrates OpenDRIVE-to-Atlas map conversion and utilizes Unreal Editor and Blender to create high-fidelity terrain and road surface models, providing vehicle dynamics feedback and closed-loop testing of physical sensors to comprehensively accelerate the validation of Level 3 and Level 4 autonomous driving algorithms. Learn more about efficient scene construction now!

In the development of ADAS and autonomous driving, the high cost and lengthy cycle of manual annotation severely hinder algorithm iteration. Hongke’s aiData Auto Annotator solution (developed by aiMotive) utilizes 4D environmental models and neural computing technology to achieve fully automated, high-precision annotation of dynamic 3D bounding boxes and static lane markings. Its annotation accuracy exceeds the human benchmark and can reduce annotation costs by more than 90%, providing an efficient and scalable closed-loop data system for autonomous driving R&D.