Autonomous driving is a challenging problem because the autonomous vehicle must
understand complex and dynamic environment. This understanding consists of predicting …

Accurate and reliable prediction of future motions of the nearby agents and effective
environment understanding will contribute to high-quality and meticulous path planning for …

This paper presents online-capable deep learning model for probabilistic vehicle trajectory
prediction. We propose a simple encoder-decoder architecture based on multihead …

It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when
planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …

For safe and efficient navigation in driving environment, autonomous vehicles are supposed
to predict future trajectories of surrounding vehicles dynamically and tackle the uncertainty of …

Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system
applications ranging from traffic simulations to driver assistance. The need for this ability is …

To accurately predict future positions of different agents in traffic scenarios is crucial for
safely deploying intelligent autonomous systems in the real-world environment. However, it …

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 …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …