Proposal to introduce provisions in UN Regulation No. 79 (Steering equipment) for the approval of Remote Control Manoeuvring (RCM) systems.

Proposal to

1. delete the requirement limiting the speed reduction during the collision-imminent warning phase
2. define a time range for the warning function to ensure adequate warning at lower speeds
3. introduce specific requirements for the achievable speed reduction for the cases of dry roads and wet roads
4. introduce speed reduction requirements at 70 km/h and 40 km/h for dry and wet roads, respectively
5. restructure provisions under “Specifications” and “Test Procedure” to enable testing at different speeds
6. prevent unintended AEBS deactivation
7. delete certain exemptions (to be addressed at national levels)

Proposal to update certain Standard Reference Test Tyre (SRTT) denominations, to remove the distinction between the two Class C3 tyres, to clarify the text and remove the reference to tyres “not marked with speed symbol”. This document will be handled by GRB during its 68th session and is referenced under GRVA due to the “conversion” of GRRF.

Proposal to introduce a definition for “Extended Mobility Tyre” based on a new “minimum requirement” performance test procedure with the relevant pictogram to be used to properly distinguish such tyres. This document will be handled by GRB during its 68th session and is referenced under GRVA due to the “conversion” of GRRF.

Proposal to amend UN R64 in line with the introduction of the “extended mobility tyre” into UN R30 (see document GRVA/2018/6). This document will be handled by GRB during its 68th session and is referenced under GRVA due to the “conversion” of GRRF.

Proposal to

1. modify the scope with regard to Category S and R agricultural vehicles,
2. modify the paragraph numbering (and related footnotes) to improve text clarity,
3. to correct an error in Annex 5 Table 7 where “35×16” should read “36×16”,
4. amend Annex 7 Part E to address variations of load-carrying capacity for tyres for construction applications (industrial tractors or skid-steers / mini-loaders) with speed category symbol “D”.

Proposal to align GTR No. 3 with technical progress, addressing among others: electromagnetic compatibility/immunity of ABS-systems, ABS performance requirements for category 3-5 vehicles (three-wheelers), uniform requirements for equipment such Emergency Stop Signal and the means to disable the ABS, if equipped. This amendment aims to harmonize GTR with recent amendments made to UN Regulation No. 78 while addressing its use under self-certification compliance systems.

Proposal to correct Revision 2 of UN R55 to align with the text of Supplement 04 to 01 series of amendments. This proposal removes a subparagraph under paragraph 13.1 that should not have been included in the Revision.

Annex 1 of UN R55 concerns the type approval communication form. Item 9.3. requires information “For Class A mechanical coupling devices or components, including towing brackets”. Poland proposes to add “if applicable” to the description in order to avoid interpretations that brackets must be part of the approval (i.e., the approval may concern the coupling ball without additional attachments).

Proposal to align the text in Annex 7 with UN R78 and to insert the reference to the L₆ and L₇ vehicle categories to respond to requests from the industry for type approval for the replacement brake lining assembly for these vehicles.

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.