With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

Deep reinforcement learning has emerged as a powerful tool for a variety of learning tasks,
however deep nets typically exhibit forgetting when learning multiple tasks in sequence. To …

Providing an efficient strategy to navigate safely through unsignaled intersections is a
difficult task that requires determining the intent of other drivers. We explore the …

Developing autonomous driving technologies necessitates addressing safety and cost
concerns. Both academic research and commercial applications of autonomous driving …

Knowledge transfer is a promising concept to achieve real-time decision-making for
autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) …

Fleet management systems (FMSs) as the pivotal part of any surface mining operation find it
essential to evolve from conventional into intelligent systems because of both Mining 4.0 …

Dense urban traffic environments can produce situations where accurate prediction and
dynamic models are insufficient for successful autonomous vehicle motion planning. We …

When applying autonomous driving technology to real-world scenarios, environmental
uncertainties make the development of decision-making algorithms difficult. Modeling the …

This paper presents a data driven approach to space object characterisation through the
application of machine learning techniques to observational light curve data. One …

Safety and cost are two important concerns for the development of autonomous driving
technologies. From the academic research to commercial applications of autonomous …