Exploring the limitations of behavior cloning for autonomous driving
Codevilla Moraes, Felipe ![Identificador ORCID](/img/uab/orcid.ico)
(Centre de Visió per Computador (Bellaterra, Catalunya))
Santana, Eder (Toyota Research Institute)
López Peña, Antonio M. ![Identificador ORCID](/img/uab/orcid.ico)
(Centre de Visió per Computador (Bellaterra, Catalunya))
Gaidon, Adrien (Toyota Research Institute)
Publicació: |
Institute of Electrical and Electronics Engineers (IEEE), cop.2019 |
Descripció: |
10 pàg. |
Resum: |
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 (e. g. , dataset bias and overfitting), new generalization issues (e. g. , 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 https://github. com/felipecode/coiltraine. |
Ajuts: |
Agencia Estatal de Investigación TIN2017-88709-R Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/FI-B1-00162
|
Nota: |
Altres ajuts: Antonio M. Lopez acknowledges the financial support by ICREA under the ICREA Academia Program. As CVC/UAB researchers, they also acknowledge the Generalitat de Catalunya CERCA Program and its ACCIO agency. |
Drets: |
Tots els drets reservats. ![](/img/licenses/InC.ico) |
Llengua: |
Anglès |
Document: |
Capítol de llibre ; recerca ; Versió acceptada per publicar |
Matèria: |
Maneuvers ;
Behavior ;
Cloning ;
Human behavior ;
Dynamic stability |
Publicat a: |
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, p. 9328-9337, ISBN 978-1-7281-4803-8 |
DOI: 10.1109/ICCV.2019.00942
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