End-to-end vision-based autonomous driving has achieved impressive success, but safety remains a major concern. The safe control problem has been addressed in low-dimensional settings using safety filters, e.g., those based on control barrier functions. Designing safety filters for vision-based controllers in the high-dimensional settings of autonomous driving can similarly alleviate the safety problem, but is significantly more challenging. In this paper, we address this challenge by using frozen pre-trained vision representation models as perception backbones to design vision-based safety filters, inspired by these models' success as backbones of robotic control policies. We empirically evaluate the offline performance of four common pre-trained vision models in this context. We try three existing methods for training safety filters for black-box dynamics, as the dynamics over representation spaces are not known. We use the DeepAccident dataset that consists of action-annotated videos from multiple cameras on vehicles in CARLA simulating real accident scenarios. Our results show that the filters resulting from our approach are competitive with the ones that are given the ground truth state of the ego vehicle and its environment.