Traffic Event Detection from Consumer Vehicle Sensor Data: An Autonomous Vehicle Study

Abstract

Traffic event detection from vehicles’ on-board sensors can detect the road information and improve traffic management and safety. Current sensor-based traffic event detection is mainly based on probe vehicles, test vehicles, or other designated vehicles, which is costly and cannot be deployed on a large scale. With the fast development of on-board equipment, data collection from consumer vehicle sensors is becoming popular and can cover a large geographic scale with almost no equipment or labor cost. However, there are very few studies of the data features and potential for application. This paper presents a pipeline for employing consumer vehicle sensors to detect roadworks. The unique data features and deficiencies of the consumer vehicle sensor are discussed and summarized. A clustering method is employed to distinguish the roadwork sites. A route builder method is proposed to reconstruct the routes of the roadworks and to extract the corresponding start and end locations. Compared with ground truth, roadworks detection from consumer vehicle sensors can cover up to 86% of the roadworks on freeways and over 40% of the roadworks on non-freeway roads. The average offset errors are 4.7% on freeways, and 24.9% on non-freeway roads. Compared with point-based roadworks detection from “ViaMichelin Traffic information,” the results from consumer vehicle sensors achieved a matching ratio of nearly 90% and were advantageous in extracting the roadworks route information. This study proves the possibility of employing consumer vehicle sensors for route-based traffic event detection and provides insights for countering uneven distribution and trajectory truncation issues related to privacy protection.