Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization

• URL: http://arxiv.org/abs/2004.06247v2
• Date: Fri, 12 Jun 2020 02:59:56 GMT
• Title: Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
• Authors: Eason Wang, Henggang Cui, Sai Yalamanchi, Mohana Moorthy, Fang-Chieh Chou, Nemanja Djuric
• Abstract summary: One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors. Methods based on top-down sceneization on one side and Generative Adrial Networks (GANs) on the other have shown to be particularly successful. In this paper we build upon these two directions and propose aversa-based conditional GAN architecture. We evaluate the proposed method on a large-scale, real-world data set, showing that it outperforms state-of-the-art GAN-based baselines.
• Score: 12.652210024012374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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