使用 VQ-VAE 和可微优化学习自动驾驶的采样分布与安全过滤器
Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization
AI总结：
本文提出利用 VQ-VAE 学习多模态轨迹采样分布，并结合可微优化安全过滤器进行轨迹修正，在密集交通场景中显著降低了碰撞率且保持了行驶速度竞争力。
摘要：
从分布中采样轨迹，然后根据指定的代价函数对其进行排序，是自动驾驶中的一种常见方法。通常，采样分布是手工设计的（例如高斯分布或网格）。最近，人们开始尝试通过条件变分自编码器（CVAE）等生成模型来学习采样分布。然而，由于 CVAE 的高斯隐变量先验，这些方法无法捕捉驾驶行为的多模态性。因此，在本文中，我们通过向量量化变分自编码器（VQ-VAE）重新构想分布学习，其离散隐空间非常适合捕捉多模态采样分布。该 VQ-VAE 使用最优轨迹的演示数据进行训练。我们进一步提出了一种基于可微优化的安全过滤器，以最小程度地修正 VQ-VAE 采样的轨迹，从而确保避免碰撞。我们在自监督学习设置中，通过对优化层进行反向传播，来学习安全过滤器的良好初始化和最优参数。我们与最先进的基于 CVAE 的基线方法在密集且激进的交通场景中进行了广泛比较，结果显示碰撞率最多降低了 12 倍，同时在行驶速度上保持竞争力。
英文摘要：
Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE). However, these approaches fail to capture the multi-modality of the driving behaviour due to the Gaussian latent prior of the CVAE. Thus, in this paper, we re-imagine the distribution learning through vector quantized variational autoencoder (VQ-VAE), whose discrete latent-space is well equipped to capture multi-modal sampling distribution. The VQ-VAE is trained with demonstration data of optimal trajectories. We further propose a differentiable optimization based safety filter to minimally correct the VQVAE sampled trajectories to ensure collision avoidance. We use backpropagation through the optimization layers in a self-supervised learning set-up to learn good initialization and optimal parameters of the safety filter. We perform extensive comparisons with state-of-the-art CVAE-based baseline in dense and aggressive traffic scenarios and show a reduction of up to 12 times in collision-rate while being competitive in driving speeds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
