Parking scenarios are spatially dense and have a lot of interactions, making predicting
vehicles' search behavior crucial and challenging for autonomous driving. Existing data …

Trajectory forecasting plays a pivotal role in the field of intelligent vehicles or social robots.
Recent works focus on modeling spatial social impacts or temporal motion attentions, but …

Predicting vehicle trajectories using deep learning has seen substantial progress in recent
years. However, making autonomous vehicles pay attention to their surrounding vehicles …

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

Trajectory prediction of surrounding vehicles is a critical task for connected and autonomous
vehicles (CAVs), helping them to realize potential dangers in the traffic environment and …

In a complex traffic environment, predicting the trajectory of surrounding vehicles in the
driver's line of sight can greatly reduce the possibility of various traffic accidents and play an …

An automated driving system should have the ability to supervise its own performance and
to request human driver to take over when necessary. In the lane keeping scenario, the …

A tree search algorithm is proposed for planning cooperative motions of multiple vehicles.
The method relies on planning techniques from artificial intelligence such as A* search and …

Operation in a real world traffic requires the ability to plan motion in complex environments
(multiple moving participants) from autonomous vehicles. Navigation through such …

With advances in automated and connected driving, secure communication is increasingly
becoming a safety-critical function. Injection of manipulated radio messages into traffic can …