Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires
reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction …

The trend of the era of the Internet of Everything has promoted the integration of various
industries and the Internet of Things (IoT) technology, and the scope of influence of the IoT is …

In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

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

Pedestrian trajectory prediction in crowd scenes plays a significant role in intelligent
transportation systems. The main challenges are manifested in learning motion patterns and …

Accurate prediction of the future trajectories of traffic agents is a critical aspect of
autonomous vehicle navigation. However, most existing approaches focus on predicting …

Accurately predicting the trajectory of road agents in complex traffic scenarios is challenging
because the movement patterns of agents are complex and stochastic, not only depending …

Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes
has become more and more significant in many intelligent systems and applications. The …

Accurate prediction of agent motion trajectories is crucial for autonomous driving,
contributing to the reduction of collision risks in human-vehicle interactions and ensuring …

Multi-modal trajectory forecasting methods commonly evaluate using single-agent metrics
(marginal metrics), such as minimum Average Displacement Error (ADE) and Final …