Autonomous Vehicle Pedestrian Detection: Minimum Safety Standards Needed to Protect Disabled Road Users

This secondary research review evaluates the extent to which current autonomous vehicle (AV) pedestrian detection datasets and validation protocols represent and protect disabled road users—including wheelchair users, white cane users, guide dog handlers, and mobility scooter users—across lighting and weather conditions. Synthesizing peer reviewed studies, standards analyses, government reports, and advocacy documents from 2015–2025, the review finds systematic underrepresentation of disability categories and accessibility infrastructure in widely used datasets, alongside documented detection biases that elevate risk for vulnerable pedestrians under low light and non standard movement scenarios. Current validation frameworks (e.g., functional safety and SOTIF) and regulatory pathways provide limited, non specific guidance on disability inclusive testing, allowing deployments that lack demonstrable parity performance for disabled pedestrians. The paper proposes a minimum pre deployment standard requiring disability inclusive dataset composition, category specific performance thresholds (with edge case coverage), and independent third party audits, with ongoing post deployment monitoring. This framework is feasible within established safety and regulatory processes and is necessary to align AV deployment with equity and safety obligations for all road users.