The prediction of pedestrian behavior is essential for automated driving in urban traffic and
has attracted increasing attention in the vehicle industry. This task is challenging because …

Accurate pedestrian trajectory prediction is significant and challenging for traffic-actor
protection and intelligent driving. However, most of the existing methods focus on pedestrian …

Predicting pedestrian trajectory is useful in many applications, such as autonomous driving
and unmanned vehicles. However, it is a challenging task because of the complexity of the …

Pedestrian trajectory prediction is one of the important research topics in the field of
computer vision and a key technology of autonomous driving system. However, it's full of …

Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very
challenging due to complex interactions between pedestrians. However, previous works …

Pedestrians are the most vulnerable road users and are at a high risk of fatal accidents.
Accurate pedestrian detection and effectively analyzing their intentions to cross the road are …

Mimicking human ability to forecast future positions or interpret complex interactions in
urban scenarios, such as streets, shopping malls or squares, is essential to develop socially …

Human trajectory forecasting in crowds presents the challenges of modelling social
interactions and outputting collision-free multimodal distribution. Following the success of …

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 …