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：预测其他道路使用者（包括车辆）的行为（即机动/轨迹），对于自动驾驶车辆（Automated Vehicles, AVs）或自动驾驶系统（Automated Driving Systems, ADSs）的安全高效运行至关重要。由于车辆未来行为存在不确定性，在给定的驾驶场景中，车辆往往存在多个未来行为模式。因此，多模态预测比单模态预测能够提供更丰富的信息，使自动驾驶车辆能够进行更优的风险评估。为此，我们提出了一种新颖的多模态预测框架，可同时预测多种可能的行为模式及其概率。该框架包括：针对机动预测的定制化问题建模、基于Transformer的新型预测模型，以及面向多模态机动与轨迹预测的定制化训练方法。使用两个公开的高速公路驾驶基准数据集（即NGSIM和highD）对框架性能进行了评估。结果表明，所提框架在预测误差方面优于文献中的最新多模态方法，且能够有效地预测合理的机动与轨迹模式。