Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

Lately, Autonomous vehicles (AV) have been gaining traction globally owing to their huge
social, economic and environmental benefits. However, the rising safety apprehensions for …

In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …

Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …

Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe
planning and navigation. However, few studies have analyzed the adversarial robustness of …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

To realize trajectory prediction, most previous methods adopt the parameter-based
approach, which encodes all the seen past-future instance pairs into model parameters …

Predicting pedestrian movement is critical for human behavior analysis and also for safe and
efficient human-agent interactions. However, despite significant advancements, it is still …

Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to
infer a finite set of future trajectories. The inferred trajectories are based on observation …