AI summaryⓘ
The authors created a new dataset called PINNS to help predict how pedestrians and vehicles interact, especially in busy and complicated traffic scenes where things are less orderly. They used videos from regular surveillance cameras that aren't tightly controlled, capturing a variety of real-world conditions like different weather and lighting across several countries. Their work addresses the lack of public data for such mixed traffic environments and includes detailed movement paths and scene info following Chinese automation standards. The dataset and their annotation method aim to help researchers improve how autonomous vehicles understand and predict movements in messy, real-life situations.
pedestrian-vehicle interactiontrajectory predictionunstructured traffic scenariosautonomous drivingdataset annotationsurveillance camerasheterogeneous agentstraffic scene analysisChinese Association of Automationmixed traffic environments
Authors
Haoyang Peng, Qian Hu, Songan Zhang, Ming Yang
Abstract
Predicting the interaction between pedestrian and vehicle is essential for autonomous driving safety in unstructured and semi-structured scenarios; however, this task is severely hindered by the scarcity of public datasets that feature dense pedestrian-vehicle interactions. Most current studies rely on structured road data, leaving the complex, heterogeneous interactions found in unstructured environments insufficiently represented and researched. In this paper, we propose a dataset annotation framework based on video data from uncalibrated surveillance cameras and present PINNS (Pedestrian-vehicle Interaction dataset from uNcalibrated cameras in uNstructured Scenes). The dataset covers multiple countries and regions, includes diverse typical traffic scenarios, and considers variations in seasons, lighting conditions, and weather. It focuses on complex scenes with dense pedestrian-vehicle interactions and is designed to be easily extensible. The dataset is constructed and annotated according to the standard issued by the Chinese Association of Automation, providing both trajectory data and corresponding scene-level information. Furthermore, this paper analyzes current challenges and research directions in heterogeneous agent trajectory prediction, shows the necessity and usefulness of the proposed dataset. We hope our framework and dataset will facilitate research on trajectory prediction and autonomous driving in complex mixed traffic scenarios. PINNS is publicly available at https://github.com/Songan-Lab.