On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset

TL;DR

This paper introduces a new dataset and method for classifying traffic signs based on their importance to autonomous vehicle path planning, emphasizing sign salience to improve safety and decision-making.

Contribution

It presents a novel sign salience feature and demonstrates its prediction using convolutional networks with experimental augmentation, enhancing autonomous driving path planning.

Findings

Achieved 76% accuracy in sign salience prediction

Sign vehicle maneuver information improves salience classification

New dataset with sign salience feature for autonomous driving

Abstract

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.