Given the current capabilities of autonomous vehicles, one can easily imagine autonomous
vehicles being released on the road in the near future. However, it can be assumed that this …

This paper proposes an architecture for integrated decision-making, motion planning, and
control in autonomous highway driving. The approach anticipates, to some degree …

When driving in urban environments, an autonomous vehicle must account for the
interaction with other traffic participants. It must reason about their future behavior, how its …

In this work we put forward a predictive trajectory planning framework to help autonomous
vehicles plan future trajectories. We develop a partially observable Markov decision process …

Self-driving vehicles need to continuously analyse the driving scene, understand the
behavior of other road users and predict their future trajectories in order to plan a safe …

This study presents a general optimal trajectory planning (GOTP) framework for autonomous
vehicles (AVs) that can effectively avoid obstacles and guide AVs to complete driving tasks …

A key issue in autonomous driving is the problem of decision logic, particularly, as it pertains
to mixed traffic involving autonomous and human-driven vehicles. With this in mind, we …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

This paper presents a novel cooperative-driving prediction and planning framework for
dynamic environments based on the methods of game theory. The proposed algorithm can …

Autonomous vehicles (AVs) must share the driving space with other drivers and often
employ conservative motion planning strategies to ensure safety. These conservative …