Exploring the limitations of behavior cloning for autonomous driving

Felipe Codevilla, Eder Santana, Antonio Lopez, Adrien Gaidon

Producció científica: Contribució a revistaArticleRecercaAvaluat per experts

351 Cites (Scopus)
1 Descàrregues (Pure)

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 url{github.com/felipecode/coiltraine/blob/master/docs/exploring-limitations.md}.

Idioma originalAnglès
Pàgines (de-a)9328-9337
Nombre de pàgines10
RevistaIEEE International Conference on Computer Vision
DOIs
Estat de la publicacióPublicada - d’oct. 2019

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