  Autonomous driving has garnered significant attention as a key research area
within artificial intelligence. In the context of autonomous driving scenarios,
the varying physical locations of objects correspond to different levels of
danger. However, conventional evaluation criteria for automatic driving object
detection often overlook the crucial aspect of an object's physical location,
leading to evaluation results that may not accurately reflect the genuine
threat posed by the object to the autonomous driving vehicle. To enhance the
safety of autonomous driving, this paper introduces a novel evaluation
criterion based on physical location information, termed PLoc. This criterion
transcends the limitations of traditional criteria by acknowledging that the
physical location of pedestrians in autonomous driving scenarios can provide
valuable safety-related information. Furthermore, this paper presents a newly
re-annotated dataset (ApolloScape-R) derived from ApolloScape. ApolloScape-R
involves the relabeling of pedestrians based on the significance of their
physical location. The dataset is utilized to assess the performance of various
object detection models under the proposed PLoc criterion. Experimental results
demonstrate that the average accuracy of all object detection models in
identifying a person situated in the travel lane of an autonomous vehicle is
lower than that for a person on a sidewalk. The dataset is publicly available
at https://github.com/lnyrlyed/ApolloScape-R.git
