Acquisition of Autonomous Reverse Parking Capability for Articulated Vehicle Using Deep Learning

In recent years, the shortage of truck drivers in domestic logistics has become apparent. One solution to this problem is the use of articulated vehicles. An articulated vehicle has a large capacity trailer, which can increase the amount of transportation per driver and help meet the transportation demands of domestic logistics. However, it requires a high level skill to drive an articulated vehicle. In particular, reverse parking of articulated vehicles requires different steering wheel maneuvers than parking of normal cars. Automation is expected to assist inexperienced drivers to operate reverse parking. In this study, we verified whether it is possible to acquire autonomous reverse parking capability using deep learning. We applied two types of deep learning methods, i.e. with and without human driving data. We used Deep Deterministic Policy Gradient (DDPG) for the method without data, and Generative Adversarial Network (GAN) for the one with data. We verified that it is possible to acquire a certain level of autonomous reverse parking capability through learning by DDPG, although it is not as good as learning using human driving data. We also verified that it is possible to acquire autonomous reverse parking capability similar to human operation through learning by GAN.