Autonomous vehicle (AV) is regarded as the ultimate solution to future automotive
engineering; however, safety still remains the key challenge for the development and …

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 …

Principles of typical motion planning algorithms are investigated and analyzed in this paper.
These algorithms include traditional planning algorithms, classical machine learning …

Autonomous vehicle (AV) technology can provide a safe and convenient transportation
solution for the public, but the complex and various environments in the real world make it …

Connected and automated vehicles (CAVs) have the potential to address a number of
safety, mobility, and sustainability issues of our current transportation systems. Cooperative …

Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic
optimization. However, the RRT* algorithm still suffers from slow convergence rate and large …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

This paper presents a novel motion planning and tracking framework for automated vehicles
based on artificial potential field (APF) elaborated resistance approach. Motion planning is …

Thanks to their road safety potential, automated vehicles are rapidly becoming a reality. In
the last decade, automated driving has been the focus of intensive automotive engineering …