… on deep reinforcement learning. Considering the traffic demands from tram and general
vehicles, it can reduce the traffic delay of general vehicles while minimizing the need for trams to …

… uncertainty from a single user, ie, intra-personal uncertainty. Type-1 fuzzy systems have been
widely used in controls and machine learning … uncertainty and inter-personal uncertainty, …

… of driverless cars and identify their key problems. Lastly, we describe the development of
deep learning with respect … The journal of machine learning research, 2016, 17(1): 2287-2318. …

… With the continuous development of computer vision, the Re-ID method of using supervised
learning suffers from the problems of strong reliance on manual annotation in the training …

… can generate this layout information properly is unclear, and how to generate … approaches
to incorporating EOP into the story generation data. Experimental results show that learning …

… for autonomous vehicles using hybrid reinforcement learning[J]. … cannot handle uncertainty,
and learning-based methods lack … reinforcement learning (HRL) approach for autonomous …

… Previously unknown curfews or other restrictions cause changes in the expected number of
… Other domains also show successful applications of DRL, such as autonomous driving [25]. …

… Eco-driving model for connected and automated vehicle platoons using the traffic capacity
remainder[J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 23-29. …

… machine learning, the perceptive intelligence of autonomous … in autonomous driving,
analyzes the effectiveness of testing on … Uncertainty-aware short-term motion prediction of traffic …

… First, to reveal the multiple uncertain factors affecting the … , such as driver’s experience,
unexpected events, and traffic condi… for reinforcement learning based autonomous driving. Front …