ICCV 2023 – Hidden Biases of End-to-End Driving Models

In this episode we discuss Hidden Biases of End-to-End Driving Models
by Bernhard Jaeger, Kashyap Chitta, Andreas Geiger. The paper discusses biases commonly found in state-of-the-art end-to-end driving systems, particularly in the context of CARLA. The first bias is a preference for target point following for lateral recovery, while the second bias involves averaging multimodal waypoint predictions for slowing down. The paper analyzes the drawbacks of these biases and proposes alternative approaches, leading to the development of TF++, a simple end-to-end method that outperforms prior work on Longest6 and LAV benchmarks.