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.

翻译：预测其他道路参与者的运动能使自动驾驶车辆进行安全高效的路径规划。该任务极为复杂，因为道路参与者的行为受多种因素影响，且未来可能轨迹的数量相当庞大（多模态）。以往大多数针对多模态运动预测的方法都基于可解释性有限的复杂机器学习系统。此外，当前基准测试所用的评估指标并未涵盖问题的所有方面，例如输出的多样性与可采纳性。在本工作中，我们基于可信赖人工智能设计的若干要求，旨在推动可信赖运动预测系统的设计进程。我们重点关注评估标准、鲁棒性以及输出的可解释性。首先，我们对评估指标进行了全面分析，识别了当前基准测试的主要不足，并提出了新的整体评估框架。接着，我们引入了一种通过模拟感知系统中的噪声来评估空间与时间鲁棒性的方法。为了增强输出的可解释性并在所提评估框架下生成更均衡的结果，我们提出了一种可附加于多模态运动预测模型的意图预测层。通过一项探究多模态轨迹与意图可视化中不同元素的用户调研，我们评估了该方法的有效性。所提出的方法及发现为开发自动驾驶汽车的可信赖运动预测系统做出了重要贡献，推动该领域朝着更高的安全性与可靠性迈进。