Accurate prediction is important for operating an autonomous vehicle in interactive scenarios. Prediction must be fast, to support multiple requests from a planner exploring a range of possible futures. The generated predictions must accurately represent the probabilities of predicted trajectories, while also capturing different modes of behaviour (such as turning left vs continuing straight at a junction). To this end, we present DiPA, an interactive predictor that addresses these challenging requirements. Previous interactive prediction methods use an encoding of k-mode-samples, which under-represents the full distribution. Other methods optimise closest-mode evaluations, which test whether one of the predictions is similar to the ground-truth, but allow additional unlikely predictions to occur, over-representing unlikely predictions. DiPA addresses these limitations by using a Gaussian-Mixture-Model to encode the full distribution, and optimising predictions using both probabilistic and closest-mode measures. These objectives respectively optimise probabilistic accuracy and the ability to capture distinct behaviours, and there is a challenging trade-off between them. We are able to solve both together using a novel training regime. DiPA achieves new state-of-the-art performance on the INTERACTION and NGSIM datasets, and improves over the baseline (MFP) when both closest-mode and probabilistic evaluations are used. This demonstrates effective prediction for supporting a planner on interactive scenarios.

翻译：准确预测对于自动驾驶车辆在交互场景中的运行至关重要。预测必须快速，以支持规划器在探索多种可能未来时发出的多个请求。生成的预测必须准确表示预测轨迹的概率，同时捕捉不同的行为模式（例如，在路口左转与直行）。为此，我们提出了交互式预测器DiPA，以应对这些具有挑战性的需求。以往的交互预测方法使用k-模态样本编码，这未能充分表示完整的分布。其他方法优化最近模态评估，即检验某个预测是否与真实轨迹相似，但允许额外的不可能预测出现，从而过度代表了不可能预测。DiPA通过使用高斯混合模型编码完整分布，并利用概率性和最近模态两种度量优化预测，解决了这些局限性。这些目标分别优化了概率准确性和捕捉不同行为的能力，且二者之间存在困难的权衡。我们通过一种新颖的训练机制同时解决了这两个问题。DiPA在INTERACTION和NGSIM数据集上达到了新的最优性能，并在同时使用最近模态和概率性评估时优于基线方法（MFP）。这证明了在交互场景中支持规划器的有效预测能力。