TY - GEN
T1 - Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions
AU - Burton, Simon
AU - Gauerhof, Lydia
AU - Hawkins, Richard David
AU - Habli, Ibrahim
AU - Sethy, Bibhuti
PY - 2019/8/9
Y1 - 2019/8/9
N2 - Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insu_ciencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study.
AB - Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insu_ciencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study.
U2 - 10.1007/978-3-030-26250-1_30
DO - 10.1007/978-3-030-26250-1_30
M3 - Conference contribution
T3 - Lecture Notes in Computer Science
SP - 365
EP - 377
BT - Computer Safety, Reliability, and Security
PB - Springer
ER -