Predicting the future behaviour of people remains an open challenge for the development of risk-aware autonomous vehicles. An important aspect of this challenge is effectively capturing the uncertainty which is inherent to human behaviour. This paper studies an approach for probabilistic motion forecasting with improved accuracy in the predicted sample likelihoods. We are able to learn multi-modal distributions over the motions of an agent solely from data, while also being able to provide predictions in real-time. Our approach achieves state-of-the-art results on the inD dataset when evaluated with the standard metrics employed for motion forecasting. Furthermore, our approach also achieves state-of-the-art results when evaluated with respect to the likelihoods it assigns to its generated trajectories. Evaluations on artificial datasets indicate that the distributions learned by our model closely correspond to the true distributions observed in data and are not as prone towards being over-confident in a single outcome in the face of uncertainty.

翻译：预测人类未来行为仍是开发风险感知自动驾驶车辆面临的开放挑战。该挑战的一个重要方面是有效捕捉人类行为固有的不确定性。本文研究了一种概率运动预测方法，在预测样本似然度方面具有更高的准确性。我们能够仅从数据中学习智能体运动的多模态分布，同时实现实时预测。当使用运动预测标准指标进行评估时，我们的方法在inD数据集上达到了最优结果。此外，在评估其生成轨迹的似然度时，该方法同样达到了最优水平。人工数据集上的评估表明，模型学习的分布与数据中的真实分布高度吻合，且在面临不确定性时不易对单一结果产生过度自信。