使用GANs和可微分轨迹栅格化改进鸟瞰模型中交通行为体的运动预测
Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
摘要：
自动驾驶难题中最关键的部分之一是预测周围交通参与者的未来运动，这使得自动驾驶汽车能够在复杂的世界中安全有效地规划其未来路线。最近，已经提出了一些算法来解决这个重要的问题，受到来自工业界和学术界的研究人员越来越大的兴趣的刺激。一方面基于自上而下的场景光栅化的方法和另一方面基于生成对抗网络（GANs）的方法已被证明是特别成功的，在交通运动预测任务上获得了最先进的精度。在本文中，我们建立在这两个方向，并提出了一个基于光栅的条件GAN架构，由一个新的可微光栅化模块在输入的条件下，将生成的轨迹映射到光栅空间中的可微的方式。这简化了任务，因为不符合场景的轨迹更容易识别，并允许梯度回流，迫使生成器输出更好，更真实的轨迹。我们在一个大规模的真实数据集上评估了所提出的方法，结果表明它的性能优于最先进的基于GAN的基线。
英文摘要：
One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors, which allows the autonomous vehicle to safely and effectively plan its future route in a complex world. Recently, a number of algorithms have been proposed to address this important problem, spurred by a growing interest of researchers from both industry and academia. Methods based on top-down scene rasterization on one side and Generative Adversarial Networks (GANs) on the other have shown to be particularly successful, obtaining state-of-the-art accuracies on the task of traffic movement prediction. In this paper we build upon these two directions and propose a raster-based conditional GAN architecture, powered by a novel differentiable rasterizer module at the input of the conditional discriminator that maps generated trajectories into the raster space in a differentiable manner. This simplifies the task for the discriminator as trajectories that are not scene-compliant are easier to discern, and allows the gradients to flow back forcing the generator to output better, more realistic trajectories. We evaluated the proposed method on a large-scale, real-world data set, showing that it outperforms state-of-the-art GAN-based baselines.
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。
