Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers

TL;DR

This paper introduces Euro-PVI, a new dataset capturing complex vehicle-pedestrian and bicyclist interactions in dense urban environments, and proposes a joint inference model that improves trajectory prediction accuracy for autonomous vehicles.

Contribution

The paper presents Euro-PVI, a diverse dataset of urban interactions, and a novel joint-$eta$-cVAE model that enhances trajectory prediction by modeling multi-agent interactions.

Findings

Euro-PVI dataset captures more complex interactions than existing datasets.

The joint-$eta$-cVAE model achieves state-of-the-art results on nuScenes and Euro-PVI.

Modeling interactions improves the accuracy of trajectory predictions.

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.