The continuous improvements of sensors' accuracy, communication frameworks, and computing technologies, have pushed the horizon of autonomous driving to a new branch of mobility, which enhances safety, efficiency, and convenience of automotive transportation. A fundamental step to the success of these systems is the design of a robust, safe, and sample-efficient decision-making module. However, real-world applications to semi-structured or even unstructured environments, such as home zones, parking valets, and narrow passages, are very limited. This paper proposes Informed Hybrid A Star (InHAS), a new computationally lightweight path planning algorithm which efficiently provides optimal paths while taking into account vehicle dimensions and satisfying non-holonomic constraints. We validate the effectiveness of the proposed method both in simulation and in real-world application.