# Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus

arXiv: https://arxiv.org/abs/1902.00487 · HTML: https://mlsanity.com/paper/1902.00487/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 25 of 25)

- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [Reactive and Safe Road User Simulations using Neural Barrier Certificates](https://mlsanity.com/paper/2109.06689/) (2021)
- [Highly Accurate and Diverse Traffic Data: The DeepScenario Open 3D Dataset](https://mlsanity.com/paper/2504.17371/) (2025)
- [Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network](https://mlsanity.com/paper/2112.06624/) (2021)
- [Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators](https://mlsanity.com/paper/2303.15231/) (2023)
- [Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes](https://mlsanity.com/paper/2409.03022/) (2024)
- [QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles](https://mlsanity.com/paper/2202.01696/) (2022)
- [Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset](https://mlsanity.com/paper/2212.06218/) (2022)
- [The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset](https://mlsanity.com/paper/2307.06165/) (2023)
- [IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2311.15193/) (2023)
- [OnSiteVRU: A High-Resolution Trajectory Dataset for High-Density Vulnerable Road Users](https://mlsanity.com/paper/2503.23365/) (2025)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [On the Generalizability of Motion Models for Road Users in Heterogeneous Shared Traffic Spaces](https://mlsanity.com/paper/2101.06974/) (2021)
- [An Automated Analysis Framework for Trajectory Datasets](https://mlsanity.com/paper/2202.07438/) (2022)
- [I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras](https://mlsanity.com/paper/2211.09342/) (2022)
- [Constellation Dataset: Benchmarking High-Altitude Object Detection for an Urban Intersection](https://mlsanity.com/paper/2404.16944/) (2024)
- [Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information](https://mlsanity.com/paper/2301.02561/) (2023)
- [Intention-Aware Decision-Making for Mixed Intersection Scenarios](https://mlsanity.com/paper/2303.17493/) (2023)
- [Characterizing Structured versus Unstructured Environments based on Pedestrians' and Vehicles' Motion Trajectories](https://mlsanity.com/paper/2502.16847/) (2025)
- [HetroD: A High-Fidelity Drone Dataset and Benchmark for Autonomous Driving in Heterogeneous Traffic](https://mlsanity.com/paper/2602.03447/) (2026)
- [Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study](https://mlsanity.com/paper/2603.19812/) (2026)
- [An NCAP-like Safety Indicator for Self-Driving Cars](https://mlsanity.com/paper/2104.00859/) (2021)
- [Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes](https://mlsanity.com/paper/2307.11259/) (2023)
- [FlyPose: Towards Robust Human Pose Estimation From Aerial Views](https://mlsanity.com/paper/2601.05747/) (2026)
- [A Pedestrian-Vehicle Interaction Benchmark and Annotation Framework for Unstructured Scenes via Uncalibrated Cameras](https://mlsanity.com/paper/2605.25947/) (2026)

## Abstract (arXiv)

Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehicle-crowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion under the influence of nearby pedestrians and the vehicle. This study proposed two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. The trajectories of pedestrians, as well as vehicles, were extracted by processing video frames that come from a down-facing camera mounted on a hovering drone as the recording equipment. The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated. The statistics of the velocity magnitude distribution demonstrated the validity of the proposed dataset. In total, there are approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian trajectories in DUT dataset. The dataset is available at GitHub.