Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it – a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We first extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We then sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state-of-the-art prediction performance, while providing improved coverage of the space of predicted trajectory semantics.

中文翻译：

---

DiversityGAN：通过潜在语义采样进行多样性感知车辆运动预测

车辆轨迹预测对于自动驾驶和高级驾驶员辅助系统至关重要。虽然现有方法可以从预测的车辆轨迹分布中采样，但它们缺乏探索它的能力——这是从规划和验证的角度评估安全性的关键能力。在这项工作中，我们设计了一种新方法来生成逼真且多样化的车辆轨迹。我们首先使用低维近似语义空间扩展生成对抗网络 (GAN) 框架，并塑造该空间以捕获合并和转向等语义。然后我们以模仿预测分布的方式从这个空间中采样，但允许我们控制语义不同结果的覆盖范围。