We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regression, occupancy maps, and 1-step stochastic policies. We instead frame the trajectory prediction problem as classification over a diverse set of trajectories. The size of this set remains manageable due to the limited number of distinct actions that can be taken over a reasonable prediction horizon. We structure the trajectory set to a) ensure a desired level of coverage of the state space, and b) eliminate physically impossible trajectories. By dynamically generating trajectory sets based on the agent's current state, we can further improve our method's efficiency. We demonstrate our approach on public, real-world self-driving datasets, and show that it outperforms state-of-the-art methods.

中文翻译：

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CoverNet：使用轨迹集进行多模态行为预测

我们提出了 CoverNet，这是一种用于城市驾驶的多模态概率轨迹预测的新方法。以前的工作采用了多种方法，包括多模态回归、占用图和 1 步随机策略。相反，我们将轨迹预测问题构建为对不同轨迹集的分类。由于可以在合理的预测范围内采取的不同行动的数量有限，因此该集合的大小仍然是可控的。我们将轨迹集构建为 a) 确保状态空间的所需覆盖水平，以及 b) 消除物理上不可能的轨迹。通过基于代理的当前状态动态生成轨迹集，我们可以进一步提高我们方法的效率。我们在公共的、真实的自动驾驶数据集上展示了我们的方法，