China input regarding the discussions on restructuring WP.29 to address the regulation of automated vehicles and the priorities for work under the 1998 Agreement.

Proposal to update the scope of the PTI informal group activities to cover new subjects, including diesel emissions system tampering and automated vehicle in-use performance.

In 2015 and 2017, public figures and experts warned about potential risks related to Artificial Intelligence use. AI has found prominent applications in the automotive sector in infotainment, vehicle management, and self-driving capability development. Some AI implications fall within WP.29’s remit, including HMI distraction and automated vehicle performance. AI in automotive applications searches for optimized solutions, looks for patterns, and predicts future events. Machine Learning, a subset of AI, uses data-based computational techniques to improve performance without explicit programming. Deep Learning uses multiple layers of nonlinear processing units for feature extraction. AI applications in the automotive sector include HD Map building, image analysis for object detection, driving policies for automated driving, and human machine interface. AI raises questions regarding model transparency, traceability, validation for safety and environmental performance functions, and whether vehicles of the same type should have identical AI performance.

Discussion document prepared under the WP.29 committee for coordination on the creation of a permanent GR Working Party dedicated to automated/autonomous and connected vehicles.

Presentation made during the ITS/Automated Driving informal group session.