%0 Conference Proceedings
%T Exploring the Limitations of Behavior Cloning for Autonomous Driving
%A Felipe Codevilla
%A Eder Santana
%A Antonio Lopez
%A Adrien Gaidon
%B 18th IEEE International Conference on Computer Vision
%D 2019
%F Felipe Codevilla2019
%O ADAS; 600.124; 600.118
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3322), last updated on Mon, 07 Dec 2020 14:11:58 +0100
%X Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, executing complex lateral and longitudinal maneuvers, even in unseen environments, without being explicitly programmed to do so. However, we confirm some limitations of the behavior cloning approach: some well-known limitations (eg, dataset bias and overfitting), new generalization issues (eg, dynamic objects and the lack of a causal modeling), and training instabilities, all requiring further research before behavior cloning can graduate to real-world driving. The code, dataset, benchmark, and agent studied in this paper can be found at github.
%U https://ieeexplore.ieee.org/document/9009463
%U http://refbase.cvc.uab.es/files/CSL2019.pdf
%U http://dx.doi.org/10.1109/ICCV.2019.00942
%P 9328-9337