Proposal to define CLIV System as the ability to derive the potential presence of a subject or object inside the car based on logic using sensing information such as pressure or capacitive sensing. CLIV System encompasses indirect sensing methods using door logic alone or door logic combined with additional sensors, indirect sensing without door logic using pressure, capacitive, temperature, motion sensors or CO₂ concentration sensor, and direct sensing using camera, millimeter wave radar, UWB radar, or WiFi sensing to detect the absolute presence of a human inside the vehicle.
Proposal to establish a new UN Regulation to help prevent children being left unattended in M2 and M3, class B and III vehicles designed specifically for the carriage of children. The regulation covers vehicles intended for child carriage and establishes minimum requirements for vehicle-based warning systems. These systems remind drivers or responsible persons to physically inspect the passenger compartment before leaving the vehicle. External visual warnings use direction indicator lamps; audible warnings use type-approved horns or reverse alarm devices, or must pass specified test procedures.
Input for agenda item 6 concerning definition and discussion on indirect plus and direct systems for CLIV-22. All references to indirect plus or direct sensing systems have been replaced by a CLIV warning system. Definitions for indirect plus or direct sensing would no longer be needed. A proposed CLIV warning system definition is presented for review, with a CLEPA proposal to modify the definition to include assessing if a child is left in a vehicle and triggering warning signals.
Change request to revise the driving function name from ‘Acceleration Control Pedal Error’ to ‘Acceleration Control for Pedal Error’ in Section 1.2.4, based on UNECE guidance that the original term is not a correct driving function name.
Proposal to add illustrative real-world application examples under Automated Driving Systems in Annex 1, items 1.2.4 and 1.3, covering low-speed unmanned delivery and sanitation vehicles in closed or semi-open areas and urban non-motorized lanes, robotaxi operation on urban roads, and autonomously-operated vehicles deployed within mining sites, ports and industrial parks.
Proposal to add to illustrative practices in para. 4.2.6.3 a reference to scenario coverage assessment based on ISO 34502 and test case generation based on ISO 34505 as sources of evidence for AI system assurance, and to add ISO 34501-34505 to Annex 4 A standards list.
Proposal to add a new risk item addressing compromised or unavailable infrastructure-sourced inputs in para. 3.2.9.5, add a corresponding extended description in Annex 2 (item 6.5), add a new table row in Annex 3, and add the term ‘Roadside Perception’ to Annex 5. AI systems that rely on roadside perception, map data, or remotely-delivered data may encounter communication outages, latency, or maliciously-altered incoming data, resulting in perception gaps or erroneous decisions.
Proposal to introduce the nine data-related safety properties defined in ISO/PAS 8800:2024 (Accuracy, Completeness, Correctness, Independence of datasets, Integrity, Representativeness, Temporality, Traceability and Verifiability) as a common verification thread across rows 4.1–4.5 in Annex 3 and Section 4.2.5, add an anchoring practice for row 4.4 (Distribution Shift) requiring periodic monitoring and reassessment of data–model drift with re-evaluation of the safety argument where drift is detected, and list digital watermarking and AI-based automated audit as candidate illustrative practices.