Generating Test Scenarios For Assessing Automated Driving Systems Using Scenario Fusion Band 28

Generating test scenarios for assessing automated driving systems using scenario fusion (Band 28)

This thesis presents the find-unify-synthesize-evaluate for representativity (FUSE4Rep) process model, a novel approach to the safety evaluation of automated driving systems (ADS). Designed to make road traffic safer by preventing accidents, ADS must demonstrate a higher level of safety than human drivers. FUSE4Rep addresses the challenge of unifying divergent information from sources such as police accident data and video-based traffic observations to ensure a comprehensive scenario representation. Through scenario fusion, the process synthesises diverse traffic data into a representative scenario catalogue, enabling a thorough assessment of ADS over a wide scenario space. Using statistical matching, it derives and varies logical scenarios to cover potential real-world conditions in stochastic simulations. A case study shows how German police accident data and video-based observations are used to create a fused scenario catalogue, demonstrating the practical application of FUSE4Rep. As part of the comprehensive “Dresden Method” for ADS evaluation, this approach provides a reliable framework for the development of safer ADS and contributes to improved road safety.

Generating Test Scenarios for Assessing Automated Driving Systems Using Scenario Fusion

Autonomous Vehicle Technology

The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.