End-to-End Style Parking Algorithm Development Pipeline Based on Procedural Parking Scenario Synthetic Data Generation

<div class="section abstract"><div class="htmlview paragraph">Less costs and higher efficiency may be constant technological pursuit. Despite the great success, data-driven AI development still requires multiple stages such as data collection, cleaning, annotation, training, and deployment to work together. We expect an end-to-end style development process that can integrate these processes, achieving an automatic data production and algorithm development process that can work with just clicks of the mouse. For this purpose, we explore an end-to-end style parking algorithm development pipeline based on procedural parking scenario synthetic data generation. Our approach allows for the automated generation of parking scenarios according to input parameters, such as scene construction, static and dynamic obstacles arrangement, material textures modification, and background changes. It then combines with the ego-vehicle trajectories into the scenarios to render high-quality images and corresponding label data based on Blender software. Utilizing Blender as render engine is one key part of our pipeline as its film-level high fidelity results guarantee the data transfer performance in real world, which makes Blender superior to solutions with game engines for parking scenario generation. We conduct experiments based on our own vehicle, automatically generated 100 parking scenes, combine expert controllers on the Software In-the-Loop platform and offline optimization algorithms to generate 1000 parking trajectories, and render more than 200000 frames end-to-end parking data. Finally, the generated data is fed into the end-to-end parking algorithm and applied in a real vehicle to park it into specific parking-slots. The experimental results demonstrate that our goal has been basically achieved, with only mouse clicks to develop a parking function.</div></div>