Proposal to modify para. 4.2.2.3.8.1 to add a requirement that a user be in a physical position to operate the driving controls, to allow gaze to be directed to multiple driving task-relevant areas rather than a single area, and to add a requirement that the user have demonstrated an appropriate response to transition of control.
Analysis of terminology related to remote support for automated driving systems, grouping terms into four categories: remote termination, remote fleet management, remote assistance, and remote driving. Remote termination disables ADS features until rescinded. Remote fleet management involves observation without performing the dynamic driving task. Remote assistance provides information while ADS continues the dynamic driving task. Remote driving involves a remote user performing the dynamic driving task. Document proposes agreeing on categories, determining definitions, developing a supplement without new requirements, and potentially developing proposals for new requirements covering ADS performance when expected remote support is not provided and further consideration of remote assistance.
Document presents initial considerations for approving Automated Driving Systems as Separate Technical Units under UN R185. It explains how component and STU approvals divide responsibility between suppliers and vehicle manufacturers, provides examples from other regulations such as lighting, tyres, glazing, and fuel systems, and discusses potential benefits and challenges specific to UN R185 application. Benefits include independent audits and avoiding repeated assessments for different vehicle types. Challenges include difficulty splitting requirements, potential information withholding, and unclear legal responsibility. Proposes flexible approaches with shared Safety Management System requirements and validation in vehicles.
Document presents research on Safety Performance Indicators (SPIs) for continuous monitoring and evaluation of Automated Driving Systems safety. Leading indicators anticipate outcomes through metrics including time-based, distance-based, probability-based and deceleration-based measures; lagging indicators confirm outcomes after occurrence such as crashes and injuries. Analysis of 21 datasets identified SHRP2 as primary source with 1402 crashes and 5262 near-crashes. Performance evaluation using Distance Headway and Time-to-Collision achieved Area Under the Curve close to 0.9 distinguishing crashes and near-crashes from normal driving. Main bottleneck identified is data quality and scarcity of reliable safety event labels.