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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.

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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.

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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.

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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.

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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.

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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.

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.

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.

Proposed objectives and guidelines for an informal working group on the physical certification of automated vehicles (i.e., proving ground tests) and audit of manufacturers (certification of the management of safety-critical aspects such as cybersecurity, data protection, and software updates).

Proposal for the goals, objectives, and principles for the development of a “real-world test drive” protocol as part of the assessment of automated vehicles.

Document prepared by the GRVA chair based upon input from various Contracting Parties on possible areas for consideration.