Our model achieves performance on par with the state-of-the-art models at a much higher prediction speed tested on multiple open datasets.
Second, we apply existing XAI methods to explain the first- and second-level models of the ensemble.
By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels.
Sharing collective perception messages (CPM) between vehicles is investigated to decrease occlusions so as to improve the perception accuracy and safety of autonomous driving.
Intersections where vehicles are permitted to turn and interact with vulnerable road users (VRUs) like pedestrians and cyclists are among some of the most challenging locations for automated and accurate recognition of road users' behavior.
In our framework, first, the spatial context between agents is explored by using self-attention architectures.
Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts.
Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic.
In inference time, we combine the past context and motion information of the target agent with samplings of the latent variables to predict multiple realistic trajectories in the future.