Pedestrian trajectory prediction is one of the main concerns of computer vision problems in
the automotive industry, especially in the field of advanced driver assistance systems. The …

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

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 …

Graph Convolutional Networks (GCN) which typically follows a neural message passing
framework to model dependencies among skeletal joints has achieved high success in …

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

Enabled by advanced data analytics and intelligent computing, augmented reality head-up
displays (AR-HUDs) are appraised with a certain degree of intelligence towards an in-car …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

Vehicle lane change behavior, which is an important part of traffic flow theory, can have a
fundamental impact on the macro and micro characteristics of traffic flow. At the same time, it …

Pedestrian trajectory prediction is an important technique of autonomous driving. In order to
accurately predict the reasonable future trajectory of pedestrians, it is inevitable to consider …

Predicting object motion behaviour is a challenging but crucial task for safe decision making
and path planning for autonomous vehicles. It is challenging in large part due to the …