  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.
