One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability.

翻译：基于愿景的驱动轨迹生成挑战之一,正在应对分配外的情景。在本文件中,我们提出了城市环境中自主车辆基于愿景的驱动轨迹生成通用方法,这可以被视为在复杂问题中推广“易变风险最小化(IRM)”方法的解决方案。我们利用对抗性学习方法将轨迹生成器培训为解码器。根据预先培训的解码器,我们推算出与轨迹相对应的潜在变量,并通过回溯推断的潜在变量对编码器进行预培训。最后,我们用最终轨迹损失来修补解码器,但微调编码器。我们比较了我们所提议的方法与最先进的轨迹生成方法以及最近关于数据集和模拟的一些域通用方法,表明我们的方法具有更好的概括能力。