Papers › Hidden Biases of End-to-End Driving Models

Hidden Biases of End-to-End Driving Models

13 Jun 2023ICCV 2023 1arXiv:2306.07957archive 2025-07-28

Bernhard Jaeger, Kashyap Chitta, Andreas Geiger

End-to-end driving systems have recently made rapid progress, in particular on CARLA. Independent of their major contribution, they introduce changes to minor system components. Consequently, the source of improvements is unclear. We identify two biases that recur in nearly all state-of-the-art methods and are critical for the observed progress on CARLA: (1) lateral recovery via a strong inductive bias towards target point following, and (2) longitudinal averaging of multimodal waypoint predictions for slowing down. We investigate the drawbacks of these biases and identify principled alternatives. By incorporating our insights, we develop TF++, a simple end-to-end method that ranks first on the Longest6 and LAV benchmarks, gaining 11 driving score over the best prior work on Longest6.

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