… input representations in the reinforcement learning setting for high-level decision making in
autonomous driving… Application to Autonomous Driving In order to model this task as a MDP, …

… This paper presents a method for observational learning in autonomous agents. A formalism
based on deep learning implementations of variational methods and Bayesian filtering …

… , structured inputs or to … learning for autonomous driving that used fully-connected network
architectures and fixed sized inputs [5], [6], [7], [8], [9] are limited in the number of vehicles that …

… the field of autonomous driving. Comparing automated vehicles to human drivers, humans
can … Long-term situation prediction for automated driving is therefore a major challenge still to …

… traffic levels due to maintenance, construction, and rehabilitation activities [50]. Autonomous
vehicles should dynamically … In Junior car [40], road blockages are avoided using a drivable …

… for scaling and accelerating the development of self-driving systems. A critical aspect of this
… framework for learning physically plausible human dynamics from real driving scenarios, …

… This work presents a constrained ADP method for autonomous driving in the constrained
dynamic environment. The framework of the integrated decision and control framework is …

… dynamic input. Their proposed Deep sets approach achieved the mean reward of 213.91 in
a highway lane change scenario containing 30 vehicles, while fixed input and … Dynamic input …

… autonomous driving. To overcome the aforementioned limitations, a novel dynamic graph
learning … are proposed in this paper for autonomous driving. The proposed method can not …

… in control of autonomous vehicles is collision avoidance. In this paper, a dynamic obstacle
… The environment in which the autonomous vehicle operates is dynamic and, if the vehicle’s …