Multimodal Manoeuvre and Trajectory Prediction for Autonomous Vehicles Using Transformer Networks

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.