The purpose of this project is to implement the Conditional Imitation Learning on various scenarios with Airsim Car
plugin and Carla
Environments. The original Conditional Imitation Learning propose a method in which a single navigatorial command is employed to take a certain action in perplexing situations. Instead, to increase the flexibility, this project concentrates on conditions similar to a path--a set of points on a plane--whereby we could present any kind of turns and velocities.
Three different strategies are assessed.
In this environment, the goal is to create an autonumous agent to drive through a tirtuous and mountainous road with the help of Behavioral Cloning
and Dataset Aggregation
approaches.
Collecting Data for Dataset Aggregation |
Autonomous Car Driving in Mountainous Environment |
2022-10-18.23.24.56.mp4
Untitled.mp4
- Codevilla, F., Müller, M., López, A., Koltun, V., and Dosovitskiy, A., “End-to-end Driving via Conditional Imitation Learning”, arXiv e-prints, 2017.
- Codevilla, F., Santana, E., Lopez, A., & Gaidon, A. (2019). Exploring the limitations of behavior cloning for autonomous driving. Proceedings of the IEEE International Conference on Computer Vision, 2019-Octob(Cvc), 9328–9337. https://doi.org/10.1109/ICCV.2019.00942
- Rhinehart, N., McAllister, R., & Levine, S. (2018). Deep Imitative Models for Flexible Inference, Planning, and Control. 1, 1–19. http://arxiv.org/abs/1810.06544