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.

Euro-PVI 数据集更好地模拟了密集城市场景中的行人和自行车与车辆的交互，我们提出 Joint-$eta$-cVAE 方法通过学习多模态共享潜在空间，更好地模拟了未来轨迹的分布，实现了在 nuScenes 和 Euro-PVI 数据集上的接近最优的结果。