Reinforcement learning (RL) is a promising direction in automated parking systems (APSs),
as integrating planning and tracking control using RL can potentially maximize the overall …

According to the existing mainstream automatic parking system (APS), a parking path is first
planned based on the parking slot detected by the sensors. Subsequently, the path tracking …

This paper proposes a vehicle-parking trajectory planning method that addresses the issues
of a long trajectory planning time and difficult training convergence during automatic …

In automatic parking motion planning, multi-objective optimization including safety, comfort,
parking efficiency, and final parking performance should be considered. Most of the current …

Automated Parking Systems (APS) are responsible for performing a parking maneuver in a
secure and time-efficient full autonomy. These systems include mainly three methods; …

Collision-free trajectory planning in narrow spaces has become one of the most challenging
tasks in automated parking scenarios. Previous optimization-based approaches can …

In this paper, a novel integrated real-time trajectory planning and tracking control framework
capable of dealing with autonomous ground vehicle (AGV) parking maneuver problems is …

Deep reinforcement learning (DRL) has been successfully adopted in many tasks, such as
autonomous driving and gaming, to achieve or surpass human-level performance. This …

This study addresses the problem of autonomous rear parking (ARP) for car-like
nonholonomic vehicles. ARP includes path planning to generate an efficient collision-free …

Purpose-The purpose of this paper is to develop a real-time trajectory planner with optimal
maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking …