Purpose For autonomous vehicles, trajectory prediction of surrounding vehicles is beneficial
to improving the situational awareness of dynamic and stochastic traffic environments, which …

Reward function, as an incentive representation that recognizes humans' agency and
rationalizes humans' actions, is particularly appealing for modeling human behavior in …

The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety
systems that aim to identify critical events such as near-crash situations and traffic violations …

In this paper, a Spatial Temporal Graph Neural Network (STGNN) model is developed,
including a temporal block and Graph Neural Network (GNN) block, to solve the problem of …

In the frameworkof human-robot collaborative assembly, it is important to predict the long-
term human hand trajectory for collision avoidance and to estimate the durations of the …

Trajectory prediction of surrounding objects plays a pivotal role in the field of autonomous
driving vehicles. In the current rollout process, it suffers from an accumulation of errors …

Anomaly detection in decision-making sequences is a challenging problem due to the
complexity of normality representation learning and the sequential nature of the task. Most …

This paper proposes a game theory-based on-ramp merging controller for connected
automated vehicles (CAVs) in mixed traffic flow. First, a two-layer decision-making …

The reliable motion prediction of all traffic participants is one of the main challenges for AVs,
especially in urban environments. Indeed, crowded driving scenarios involve strong and …

Finding optimal paths for spatial search and map exploration problems are NP-hard. Since
spatial search and environmental exploration are parts of human central activities, learning …