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Predicting the behaviour (i.e., manoeuvre/trajectory) of other road users,including vehicles, is critical for the safe and efficient operation ofautonomous vehicles (AVs), a.k.a., automated driving systems (ADSs). Due to theuncertain future behaviour of vehicles, multiple future behaviour modes areoften plausible for a vehicle in a given driving scene. Therefore, multimodalprediction can provide richer information than single-mode prediction, enablingAVs to perform a better risk assessment. To this end, we propose a novelmultimodal prediction framework that can predict multiple plausible behaviourmodes and their likelihoods. The proposed framework includes a bespoke problemformulation for manoeuvre prediction, a novel transformer-based predictionmodel, and a tailored training method for multimodal manoeuvre and trajectoryprediction. The performance of the framework is evaluated using three publichighway driving datasets, namely NGSIM, highD, and exiD. The results show thatour framework outperforms the state-of-the-art multimodal methods in terms ofprediction error and is capable of predicting plausible manoeuvre andtrajectory modes.

Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks