Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks
This addresses the need for safer and more efficient autonomous vehicles by improving risk assessment through multimodal prediction, though it is incremental as it builds on existing transformer-based methods.
The paper tackles the problem of predicting multiple plausible future behaviors of vehicles for autonomous driving by proposing a novel multimodal prediction framework, which outperforms state-of-the-art methods on three public highway datasets in terms of prediction error.
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.