End-to-end Learning Method for Self-Driving Cars with Trajectory Recovery Using a Path-following Function

We propose an end-to-end learning method for autonomous driving systems in this article. End-to-end model estimates an appropriate motor command from raw sensory signals. End-to-end model for autonomous driving systems has recently been based on neural networks, which are popular for their good recognition ability. A common problem is how to return a car to the driving lane when the car goes off the track. In our research, we collect recovery data based on the distance from a desired track (the nearest waypoint link) during a road test with a simulator. To train the recovery behavior, instead of collecting human driving data, we use a path-following module (which means the car automatically drives on a pre-decided route using the car's current position). Our proposed method is divided into three phases. In phase 1, we collect data only using a path-following module during 100 laps of driving. In phase 2, we generate driving behavior using a neural driving module trained by the data collected in phase 1. This includes switching between the accelerator, brake and steering based on a threshold. We collect further data on the recovery behavior using the path-following module during 100 laps of driving. In phase 3, we generate driving behavior using the neural driving module trained by the data collected in phases 1 and 2. To assess the proposed method, we compared the average distance from the nearest waypoint link and the average distance traveled per lap for datasets with no recovery, for datasets with random recovery, and for datasets for the proposed method with recovery. A model based on the proposed method drove well and paid more attention to the road rather than the sky and other unrelated objects automatically for both untrained and trained courses and weather.