  Motion prediction of road users in traffic scenes is critical for autonomous
driving systems that must take safe and robust decisions in complex dynamic
environments. We present a novel motion prediction system for autonomous
driving. Our system is based on the Bayesian inverse planning framework, which
efficiently orchestrates map-based goal extraction, a classical control-based
trajectory generator and a mixture of experts collection of light-weight neural
networks specialised in motion profile prediction. In contrast to many
alternative methods, this modularity helps isolate performance factors and
better interpret results, without compromising performance. This system
addresses multiple aspects of interest, namely multi-modality, motion profile
uncertainty and trajectory physical feasibility. We report on several
experiments with the popular highway dataset NGSIM, demonstrating
state-of-the-art performance in terms of trajectory error. We also perform a
detailed analysis of our system's components, along with experiments that
stratify the data based on behaviours, such as change-lane versus follow-lane,
to provide insights into the challenges in this domain. Finally, we present a
qualitative analysis to show other benefits of our approach, such as the
ability to interpret the outputs.

Robotics