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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2305.18942 |
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| _version_ | 1866910285913128960 |
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| author | Stoll, Martin Mazzola, Markus Dolgov, Maxim Mathes, Jürgen Möser, Nicolas |
| author_facet | Stoll, Martin Mazzola, Markus Dolgov, Maxim Mathes, Jürgen Möser, Nicolas |
| contents | We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or highly realistic simulation. Focusing on a roundabout scenario, we show that this requirement can be relaxed in favour of targeted, simplistic simulated data. A benefit is that such data can be easily generated for critical scenarios that are typically underrepresented in realistic datasets. By applying vanilla behavioural cloning almost exclusively to lightweight simulated data, we achieve reliable and comfortable driving in a real-world test vehicle. We leverage an incremental development approach that includes regular in-vehicle testing to identify sim-to-real gaps, targeted data augmentation, and training scenario variations. In addition to a detailed description of the methodology, we share our lessons learned, touching upon scenario generation, simulation features, and evaluation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_18942 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Scaling Planning for Automated Driving using Simplistic Synthetic Data Stoll, Martin Mazzola, Markus Dolgov, Maxim Mathes, Jürgen Möser, Nicolas Robotics We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or highly realistic simulation. Focusing on a roundabout scenario, we show that this requirement can be relaxed in favour of targeted, simplistic simulated data. A benefit is that such data can be easily generated for critical scenarios that are typically underrepresented in realistic datasets. By applying vanilla behavioural cloning almost exclusively to lightweight simulated data, we achieve reliable and comfortable driving in a real-world test vehicle. We leverage an incremental development approach that includes regular in-vehicle testing to identify sim-to-real gaps, targeted data augmentation, and training scenario variations. In addition to a detailed description of the methodology, we share our lessons learned, touching upon scenario generation, simulation features, and evaluation metrics. |
| title | Scaling Planning for Automated Driving using Simplistic Synthetic Data |
| topic | Robotics |
| url | https://arxiv.org/abs/2305.18942 |