Uncertainty plays a key role in future prediction. The future is uncertain. That means there might be many possible futures. A future prediction method should cover the whole possibilities to be robust. In autonomous driving, covering multiple modes in the prediction part is crucially important to make safety-critical decisions. Although computer vision systems have advanced tremendously in recent years, future prediction remains difficult today. Several examples are uncertainty of the future, the requirement of full scene understanding, and the noisy outputs space. In this thesis, we propose solutions to these challenges by modeling the motion explicitly in a stochastic way and learning the temporal dynamics in a latent space.
翻译:不确定性在未来的预测中起着关键作用。 未来是不确定的。 这意味着可能有许多未来。 未来的预测方法应该覆盖充满活力的全方位可能性。 在自主驱动中, 在预测部分涵盖多种模式对于做出安全批评性决定至关重要。 尽管计算机视觉系统近年来取得了巨大进步,但未来的预测在今天仍然困难重重。 几个例子包括未来的不确定性、 全面了解现场的要求和噪音的输出空间。 在这个论点中,我们提出应对这些挑战的解决方案,方法是明确以随机方式模拟该动议,并学习潜藏空间的时间动态。