Predicting the future trajectories of surrounding vehicles is a key competence for safe and
efficient real-world autonomous driving systems. Previous works have presented deep …

Despite the advancement in the technology of autonomous driving cars, the safety of a self-
driving car is still a challenging problem that has not been well studied. Motion prediction is …

With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …

As autonomous driving technology progresses, the need for precise trajectory prediction
models becomes paramount. This paper introduces an innovative model that infuses …

Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous
vehicles being released on the road in the near future. However, it can be assumed that this …

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …

Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …

In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …