Imitation learning holds great promise for addressing the complex task of autonomous urban driving, as experienced human drivers can navigate highly challenging scenarios with ease. While behavior cloning is a widely used imitation learning approach in autonomous driving due to its exemption from risky online interactions, it suffers from the covariate shift issue. To address this limitation, we propose a context-conditioned imitation learning approach that employs a policy to map the context state into the ego vehicle's future trajectory, rather than relying on the traditional formulation of both ego and context states to predict the ego action. Additionally, to reduce the implicit ego information in the coordinate system, we design an ego-perturbed goal-oriented coordinate system. The origin of this coordinate system is the ego vehicle's position plus a zero mean Gaussian perturbation, and the x-axis direction points towards its goal position. Our experiments on the real-world large-scale Lyft and nuPlan datasets show that our method significantly outperforms state-of-the-art approaches.

翻译：模仿学习在解决复杂的城市自动驾驶任务中展现出巨大潜力，经验丰富的人类驾驶员可以轻松应对极具挑战性的场景。尽管行为克隆因其无需进行高风险在线交互而成为自动驾驶中广泛使用的模仿学习方法，但它存在协变量偏移问题。为解决这一局限，我们提出了一种上下文条件模仿学习方法，该方法利用策略将上下文状态映射到自车的未来轨迹，而非依赖传统方法中同时使用自车和上下文状态来预测自车动作。此外，为减少坐标系中的隐含自车信息，我们设计了一种基于自车扰动的目标导向坐标系。该坐标系的原点为自车位置加上零均值高斯扰动，x轴方向指向其目标位置。我们在真实世界大规模Lyft和nuPlan数据集上的实验表明，我们的方法显著优于现有最先进方法。