Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration

Abstract

Applying reinforcement learning algorithms to autonomous driving is difficult because of mismatches between the simulation in which the algorithm was trained and the real world. To address this problem, data from global navigation satellite systems and inertial navigation systems (GNSS/INS) were used to gather pseudolabels for semantic segmentation. A very simple dynamics model was used as a simulator, and dynamic parameters were obtained from the linear regression of manual driving records. Segmentation and a dynamic calibration method were found to be effective in easing the transition from a simulation to the real world. Pseudosegmentation labels are found to be more suitable for reinforcement learning models. We conducted tests on the efficacy of our proposed method, and a vehicle using the proposed system successfully drove on an unpaved track for approximately 1.8 km at an average speed of 26.57 km/h without incident.