This work introduces a method for selecting test cases from large-scale naturalistic driving studies to validate automated driving systems. It balances representativeness and coverage using a kernel-based approach, enabling fair comparisons with human drivers and supporting efficient safety validation.
- Chen Qian
- Jingbin Xu
- Feng Guo
