On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset
This work addresses the challenge of prioritizing traffic sign information for safer autonomous driving, though it is incremental as it builds on existing methods with a new dataset feature.
The paper tackled the problem of identifying which traffic signs are most relevant for autonomous vehicle path planning by introducing a novel dataset with sign salience as a key feature, achieving 76% accuracy in predicting sign salience using convolutional networks combined with vehicle maneuver information.
Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents. While all static scene elements are a source of information, there is asymmetric importance to the information available to the ego vehicle. We present a dataset with a novel feature, sign salience, defined to indicate whether a sign is distinctly informative to the goals of the ego vehicle with regards to traffic regulations. Using convolutional networks on cropped signs, in tandem with experimental augmentation by road type, image coordinates, and planned maneuver, we predict the sign salience property with 76% accuracy, finding the best improvement using information on vehicle maneuver with sign images.