Human Driver Modeling and Prediction

This page shows the dataset and papers for modeling and prediction of human behavior in tight-constrained environments.

Dragon Lake Parking (DLP) Dataset

The Dragon Lake Parking (DLP) Dataset contains annotated video and data of vehicles, cyclists, and pedestrians inside a parking lot. We collected it by flying a drone above a huge parking lot.

Abundant vehicle parking maneuvers and interactions are recorded. To the best of our knowledge, this is the first and largest public dataset designated for the parking scenario (up to April 2022), featuring high data accuracy and a rich variety of realistic human driving behavior.

Papers

Shen, X., Lacayo, M., Guggilla, N., & Borrelli, F. (2022). ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer. arXiv preprint arXiv:2204.10777. Accepted at 2022 IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022)
Shen, X., Batkovic, I., Govindarajan, V., Falcone, P., Darrell, T., & Borrelli, F. (2020, October). Parkpredict: Motion and intent prediction of vehicles in parking lots. In 2020 IEEE Intelligent Vehicles Symposium (IV) (pp. 1170-1175). IEEE.

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