PLoc: A New Evaluation Criterion Based on Physical Location for Autonomous Driving Datasets
This work addresses safety evaluation in autonomous driving by incorporating physical location into criteria, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem that conventional object detection evaluation criteria for autonomous driving ignore the physical location of objects, which affects safety assessment, by introducing a new evaluation criterion called PLoc and a re-annotated dataset ApolloScape-R; experimental results show that object detection models have lower average accuracy for pedestrians in travel lanes compared to those on sidewalks.
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