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 two public benchmark highway driving datasets, namely NGSIM and highD. The results show that the proposed framework outperforms the state-of-the-art multimodal methods in the literature in terms of prediction error and is capable of predicting plausible manoeuvre and trajectory modes.

翻译：自动驾驶系统（Automated Driving Systems, ADS）中，预测车辆和其他道路用户行为（即动作/轨迹）是确保安全高效运行的关键。由于车辆的未来行为不确定，因此在给定的驾驶场景中，一个车辆往往存在多种未来行为可能性。因此，多模态预测可以提供比单模态预测更丰富的信息，使ADS能够更好地进行风险评估。为此，我们提出了一种新颖的多模态预测框架，用于预测多个可能的行驶模式和其可能性。该框架包括专门针对行驶轨迹预测的问题形式、一种新颖的基于Transformer的预测模型和针对多模态行驶轨迹和动作预测的定制化训练方法。我们使用两个公共基准公路驾驶数据集（NGSIM和highD）来评估该框架的性能。结果显示，该框架在预测误差方面优于文献中的最先进的多模态方法，并能够预测出合理的行驶模式和轨迹。