Vehicle motion planning is an essential component of autonomous driving technology.
Current rule-based vehicle motion planning methods perform satisfactorily in common …

Trajectory prediction in autonomous driving relies on accurate representation of all relevant
contexts of the driving scene including traffic participants, road topology, traffic signs as well …

Significant progress has been made in training multimodal trajectory forecasting models for
autonomous driving. However, effectively integrating these models with downstream …

Trajectory prediction is essential for intelligent autonomous systems like autonomous
driving, behavior analysis, and service robotics. Deep learning has emerged as the …

We present JointMotion, a self-supervised learning method for joint motion prediction in
autonomous driving. Our method includes a scene-level objective connecting motion and …

Fully autonomous vehicles have the potential to greatly reduce vehicular accidents and
revolutionize how people travel and how we transport goods. Many of the major challenges …

Deep reinforcement learning (RL) has shown significant advancements in various domains.
Building a reusable library is essential to accelerate the research and development process …

Decision-making is a fundamental yet challenging task for autonomous vehicles, as it
requires accurate predictions of other traffic participants and, above all, safe and interactive …

Deep neural network (DNN) video analytics is crucial for autonomous systems such as self-
driving vehicles, unmanned aerial vehicles (UAVs), and security robots. However, real-world …

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By
reasoning about the possible behavior of other target agents, the ego vehicle (EV) can …