Understanding and predicting human motion behavior with social interactions have become
an increasingly crucial problem for a vast number of applications, ranging from visual …

Predicting trajectories of pedestrians based on goal information in highly interactive scenes
is a crucial step toward Intelligent Transportation Systems and Autonomous Driving. The …

Reliable anticipation of pedestrian trajectory is imperative for the operation of autonomous
vehicles and can significantly enhance the functionality of advanced driver assistance …

Pedestrian trajectory prediction is a critical research area with numerous domains, eg, blind
navigation, autonomous driving systems, and service robots. There exist two challenges in …

The trajectory prediction is significant for the decision-making of autonomous driving
vehicles. In this paper, we propose a model to predict the trajectories of target agents around …

The paper proposes a motion planning method using a deep reinforcement learning
algorithm, Asynchronous Advantage Actor-Critic (A3C). For mobile robot navigation tasks in …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

We focus on robot navigation in crowded environments. To navigate safely and efficiently
within crowds, robots need models for crowd motion prediction. Building such models is …

Pedestrian trajectory prediction is crucial for many important applications. This problem is a
great challenge because of complicated interactions among pedestrians. Previous methods …

Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical
problem in numerous applications, such as self-driving cars and socially aware robots …