Home > Books and collections > Book chapters > Exploring the limitations of behavior cloning for autonomous driving |
Imprint: | Institute of Electrical and Electronics Engineers (IEEE), cop.2019 |
Description: | 10 pàg. |
Abstract: | 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. |
Grants: | Agencia Estatal de Investigación TIN2017-88709-R Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/FI-B1-00162 |
Note: | 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. |
Rights: | Tots els drets reservats. |
Language: | Anglès |
Document: | Capítol de llibre ; recerca ; Versió acceptada per publicar |
Subject: | Maneuvers ; Behavior ; Cloning ; Human behavior ; Dynamic stability |
Published in: | 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019, p. 9328-9337, ISBN 978-1-7281-4803-8 |
Postprint 11 p, 1.5 MB |