With the objective to improve road safety, the automotive industry is moving toward more
“intelligent” vehicles. One of the major challenges is to detect dangerous situations and react …

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …

Accurately discovering hazards and issuing appropriate warnings to drivers in advance or
performing autonomous control is the core of the Collision Avoidance (CA) system used to …

The recent progress in autonomous vehicle research and development has led to
increasingly widespread testing of fully autonomous vehicles on public roads, where …

By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract
representation which allows us to apply Graph Neural Network (GNN) models for traffic …

Accurate trajectory prediction of surrounding road users is critical to autonomous driving
systems. In mixed traffic flows, road users with different kinds of behaviors and styles bring …

Advanced Driver Assistance Systems have been shown to greatly improve road safety.
However, existing systems are typically reactive with an inability to understand complex …

Forecasting vehicles' future motion is crucial for real-world applications such as the
navigation of autonomous vehicles and feasibility of safety systems based on the Internet of …

This paper presents a surround vehicle motion prediction algorithm for multi-lane turn
intersections using a Long Short-Term Memory (LSTM)-based Recurrent Neural Network …

Connected automated driving technologies have shown tremendous improvement in recent
years. However, it is still not clear how driving behaviors and energy consumption correlate …