Predicting the behaviour (i.e., manoeuvre/trajectory) of other road users, including vehicles, is critical for the safe and efficient operation of autonomous vehicles (AVs), a.k.a., automated driving systems (ADSs). Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for a vehicle in a given driving scene. Therefore, multimodal prediction can provide richer information than single-mode prediction, enabling AVs to perform a better risk assessment. To this end, we propose a novel multimodal prediction framework that can predict multiple plausible behaviour modes and their likelihoods. The proposed framework includes a bespoke problem formulation for manoeuvre prediction, a novel transformer-based prediction model, and a tailored training method for multimodal manoeuvre and trajectory prediction. The performance of the framework is evaluated using three public highway driving datasets, namely NGSIM, highD, and exiD. The results show that our framework outperforms the state-of-the-art multimodal methods in terms of prediction error and is capable of predicting plausible manoeuvre and trajectory modes.

翻译：预测其他道路使用者（包括车辆）的行为（即机动/轨迹）对于自动驾驶汽车（AVs）的安全高效运行至关重要，自动驾驶汽车也被称为自动驾驶系统（ADSs）。由于车辆未来行为的不确定性，在给定驾驶场景中，车辆通常存在多种可能的未来行为模式。因此，多模态预测能比单模态预测提供更丰富的信息，使自动驾驶汽车能够进行更好的风险评估。为此，我们提出了一种新颖的多模态预测框架，该框架能够预测多种合理的行为模式及其可能性。该框架包括针对机动预测的定制问题公式、基于Transformer的新型预测模型，以及针对多模态机动与轨迹预测的定制训练方法。使用三个公开的高速公路驾驶数据集（NGSIM、highD和exiD）对框架性能进行了评估。结果表明，我们的框架在预测误差方面优于现有最先进的多模态方法，并且能够预测出合理的机动与轨迹模式。