One of the major challenges in cities around the globe nowadays is the availability of sufficient parking space. High Density Parking (HDP) addresses this issue by providing efficient techniques for usage of parking areas. In order to realize HDP, automated vehicles need to be maneuvered in densely occupied environments. This poses another challenge of generating preferably optimal, collision-free trajectories that the vehicles can follow. The generation of trajectories involves motion planning, which is known to be a difficult task for vehicles due to their nonholonomic nature. This thesis focuses on motion planning involving cooperation of vehicles in dense parking environments and employs accurate collision checking to meet the requirements imposed by such parking scenarios. For this purpose, a heuristic search-based motion planner Space Exploration Guided Heuristic Search (SEHS) [10] is adapted, implemented and benchmarked with the sampling-based motion planners adapted from Rapidly-Exploring Random Trees (RRT) [28] and Sparse Roadmap Spanners (SPARS) [13]. When given the initial and final poses of the vehicles, together with their shapes and those of the surrounding obstacles, the planners return feasible trajectories for the vehicles between their respective start and goal poses. All the planners except the RRT adaptations are capable of trajectory optimization. The objective of these planners is to reduce the maximum trajectory time among all the vehicles, so that the whole system has the minimum execution time, which can be seen as one of the bottlenecks for such an automated parking system. The SEHS and RRT adaptations use the kinodynamic approach for motion planning, whereas the SPARS adaptation uses geometric planning. One of the adaptations of RRT uses a centralized approach, while all the other planners use a decoupled approach for cooperative motion planning.