… This study proposes a framework for human-like autonomous car-following planning based
on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation …

… autonomous driving at intersections. This approach integrates perception, decision-making,
and planning into a unified model to learn driving strategies and behavioral planning directly …

… predict driver behaviors and responses to desired actions in Figure 1, in a similar manner to
… in a trajectory planning algorithm, we aim to assist the integration of autonomy into society. …

… planning and tracking algorithms of the autonomous vehicles conforming to various driving
styles, and the scenarios of autonomous driving include car-… for autonomous cars considering …

… transitions between states of cars from time to time. Such a smooth speed profile with these
bounds does not only exhibit energy-saving behaviors of autonomous cars but also presents …

… AUTONOMOUS cars, also known as driver-less cars, are vehicles that can sense its
environment and operate without human input. In autonomous driving, there is no need of a human …

… In the autonomous vehicles domain, [14] proposed an HRL-based method for behavior
planning with high-level options and low-level actions. Furthermore, in order to improve sample …

… forward simulation that the other vehicle will react to the planned trajectory of the ego vehicle
and eg let our autonomous car cross the intersections first if it reaches the intersection first. …

… Our autonomous car platform. The vehicle is a Ford Fusion equipped with a sensor suite …
In future work we plan to explore explicitly modeling unexpected behavior, such as the …

… behaviors in driving while they may reduce traffic flow efficiency and improve possibility of
traffic accident [7]. This paper will figure out how autonomous … changing behavior happens in, …