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.

翻译：从分布中采样轨迹并依据指定代价函数进行排序是自动驾驶中的常用方法。传统上，采样分布是手工设计的（例如高斯分布或网格分布）。近期，部分研究尝试通过条件变分自编码器（CVAE）等生成模型学习采样分布。然而，由于CVAE的高斯潜先验约束，这些方法无法捕捉驾驶行为的多模态特性。为此，本文重新构想通过矢量量化变分自编码器（VQ-VAE）进行分布学习——其离散潜空间能有效捕捉多模态采样分布。VQ-VAE使用最优轨迹的演示数据进行训练。我们进一步提出基于可微优化的安全滤波器，以最小化修正VQ-VAE采样的轨迹，确保避碰功能。在自监督学习框架下，通过优化层的反向传播，学习安全滤波器的良好初始化和最优参数。我们在密集激进交通场景中与基于CVAE的先进基线方法进行广泛对比，结果表明碰撞率降低达12倍的同时，驾驶速度具有竞争力。