E2E Parking: Autonomous Parking by the End-to-end Neural Network on the CARLA Simulator

Autonomous parking is a crucial application for intelligent vehicles, especially in crowded parking lots. The confined space requires highly precise perception, planning, and control. Currently, the traditional Automated Parking Assist (APA) system, which utilizes geometric-based perception and rule-based planning, can assist with parking tasks in simple scenarios. With noisy measurement, the handcrafted rule often lacks flexibility and robustness in various environments, which performs poorly in super crowded and narrow spaces. On the contrary, there are many experienced human drivers, who are good at parking in narrow slots without explicit modeling and planning. Inspired by this, we expect a neural network to learn how to park directly from experts without handcrafted rules. Therefore, in this paper, we present an end-to-end neural network to handle parking tasks. The inputs are the images captured by surrounding cameras and basic vehicle motion state, while the outputs are control signals, including steer angle, acceleration, and gear. The network learns how to control the vehicle by imitating experienced drivers. We conducted closed-loop experiments on the CARLA Simulator to validate the feasibility of controlling the vehicle by the proposed neural network in the parking task. The experiment demonstrated the effectiveness of our end-to-end system in achieving the average position and orientation errors of 0.3 meters and 0.9 degrees with an overall success rate of 91%. The code is available at: https://github.com/qintonguav/e2e-parking-carla