  Predicting the motion of other road agents enables autonomous vehicles to
perform safe and efficient path planning. This task is very complex, as the
behaviour of road agents depends on many factors and the number of possible
future trajectories can be considerable (multi-modal). Most prior approaches
proposed to address multi-modal motion prediction are based on complex machine
learning systems that have limited interpretability. Moreover, the metrics used
in current benchmarks do not evaluate all aspects of the problem, such as the
diversity and admissibility of the output. In this work, we aim to advance
towards the design of trustworthy motion prediction systems, based on some of
the requirements for the design of Trustworthy Artificial Intelligence. We
focus on evaluation criteria, robustness, and interpretability of outputs.
First, we comprehensively analyse the evaluation metrics, identify the main
gaps of current benchmarks, and propose a new holistic evaluation framework. We
then introduce a method for the assessment of spatial and temporal robustness
by simulating noise in the perception system. To enhance the interpretability
of the outputs and generate more balanced results in the proposed evaluation
framework, we propose an intent prediction layer that can be attached to
multi-modal motion prediction models. The effectiveness of this approach is
assessed through a survey that explores different elements in the visualization
of the multi-modal trajectories and intentions. The proposed approach and
findings make a significant contribution to the development of trustworthy
motion prediction systems for autonomous vehicles, advancing the field towards
greater safety and reliability.
