Efficiently Build An Accurate Curbside Parking Rule Database for Autonomous Vehicle

Understanding curbside parking rules is crucial for drivers to quickly find legal curbside parking. Currently, data providers rely on manual methods to collect information about curbside parking signs, either by individually noting down sign details or downloading street-level imagery from digital maps. However, this process is slow and inadequate for building an accurate and up-to-date curbside parking rule database, considering the frequent updates to parking signs. In this paper, we propose a novel deep-learning-based system that automatically generates the curbside parking rule database using videos captured by off-the-shelf dashcams installed on vehicles. To the best of our knowledge, our system is the first of its kind to detect and interpret real-world curbside parking signs from videos and generate parking rules. Through real-world evaluations, we demonstrate that our system efficiently constructs an accurate curbside parking rule database.