This paper proposes a novel deep learning framework for multi-modal motion prediction.
The framework consists of three parts: recurrent neural network to process target agent's …

To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of
other agents around it. Motion prediction is an extremely challenging task that recently …

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety
of autonomous driving. Recent motion prediction approaches attempt to achieve such …

Automated Vehicles (AVs) have been receiving increasing attention as a potential highly
mechanised, intelligent, self-regulating futuristic mode of transport. AVs are predicted to …

Purpose-The purpose of this paper aims to model interaction relationship of traffic agents for
motion prediction, which is critical for autonomous driving. It is obvious that traffic agents' …

Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …

Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …

The multi-source visual information from ring cameras and stereo cameras provides a direct
observation of the road, traffic conditions, and vehicle behavior. However, relying solely on …

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 …

Accurate trajectory prediction is an essential task in automated driving, which is achieved by
sensing and analyzing the behavior of surrounding vehicles. Although plenty of research …