# Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers

Source: [Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers](<https://www.sota2.com/research/paper/68fa6a381e209131cea26aeb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T13:38:13.231Z

Authors: Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz, Bernt Schiele

Published: 2021-06-22T15:40:21.000Z

## Abstract

Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle and pedestrian or bicyclist have a significant impact on the trajectories of traffic participants e.g. stopping or turning to avoid collisions. Although recent datasets and trajectory prediction approaches have fostered the development of autonomous vehicles yet the amount of vehicle-pedestrian (bicyclist) interactions modeled are sparse. In this work, we propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In particular, our dataset caters more diverse and complex interactions in dense urban scenarios compared to the existing datasets. To address the challenges in predicting future trajectories with dense interactions, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. This enables our Joint-$\\beta$-cVAE approach to better model the distribution of future trajectories. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Trajectory Prediction on nuScenes (test)](<https://www.sota2.com/research/sota/trajectory-prediction-on-nuscenes-test.md>): Trajectory Prediction; nuScenes (test); Min Displacement Error (1s): 0.01.

- [Trajectory Prediction on Euro-PVI (test)](<https://www.sota2.com/research/sota/trajectory-prediction-on-euro-pvi-test.md>): Trajectory Prediction; Euro-PVI (test); BoN@20 (t+1s): 10.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.12442>)

[PDF](<https://arxiv.org/pdf/2106.12442.pdf>)