Comparing Output Modalities in End-to-End Driving

Abstract

Self-driving car technology has made significant steps in the last ten years with the advancements in neural networks. The first autonomous vehicles are driving in San Francisco and Beijing. One of the promising approaches is end-to-end driving, where a neural network transforms an input image from a camera to output commands to control the vehicle. The most common output modalities are steering angle and trajectory. Both have been extensively benchmarked but not compared in similar settings. Metrics are usually calculated off-policy using a separated test dataset or on-policy using a simulator, but these have proven to correlate weakly with real-life performance. In this thesis, the comparison is made using an autonomous vehicle driving on WRC Rally Estonia tracks. The results show that the trajectory prediction approach is better at road positioning and recovering from non-ideal trajectories, which results in fewer situations where the safety driver has to take over.