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.

翻译：我们挑战了普遍认为的共识，即应用深度学习解决自动驾驶规划任务必然需要大量的真实世界数据或高度逼真的仿真。聚焦于环岛场景，我们证明这种要求可以放宽，转而采用有针对性的简单仿真数据。其优势在于，对于在真实数据集中通常代表性不足的关键场景，可以轻松生成此类数据。通过几乎完全将普通行为克隆应用于轻量级仿真数据，我们在真实测试车辆中实现了可靠且舒适的驾驶。我们采用增量开发方法，包括定期进行实车测试以识别仿真到现实的差距、有针对性的数据增强以及训练场景变化。除详细描述方法论外，我们还分享了经验教训，涉及场景生成、仿真特征和评估指标。