Machine learning has rapidly evolved during the last decade, achieving expert human performance on notoriously challenging problems such as image classification. This success is partly due to the re-emergence of bio-inspired modern artificial neural networks (ANNs) along with the availability of computation power, vast labelled data and ingenious human-based expert knowledge as well as optimisation approaches that can find the correct configuration (and weights) for these networks. Neuroevolution is a term used for the latter when employing evolutionary algorithms. Most of the works in neuroevolution have focused their attention in a single type of ANNs, named Convolutional Neural Networks (CNNs). Moreover, most of these works have used a single optimisation approach. This work makes a progressive step forward in neuroevolution for vehicle trajectory prediction, referred to as neurotrajectory prediction, where multiple objectives must be considered. To this end, rich ANNs composed of CNNs and Long-short Term Memory Network are adopted. Two well-known and robust Evolutionary Multi-objective Optimisation (EMO) algorithms, NSGA-II and MOEA/D are also adopted. The completely different underlying mechanism of each of these algorithms sheds light on the implications of using one over the other EMO approach in neurotrajectory prediction. In particular, the importance of considering objective scaling is highlighted, finding that MOEA/D can be more adept at focusing on specific objectives whereas, NSGA-II tends to be more invariant to objective scaling. Additionally, certain objectives are shown to be either beneficial or detrimental to finding valid models, for instance, inclusion of a distance feedback objective was considerably detrimental to finding valid models, while a lateral velocity objective was more beneficial.

翻译：机器学习在过去十年中迅速发展，在图像分类等极具挑战性的问题上达到了人类专家水平。这一成功部分归功于受生物启发的现代人工神经网络（ANN）的重新兴起，以及计算能力、大量标注数据、基于人类专家的巧妙知识，以及能够为这些网络找到正确配置（和权重）的优化方法的可用性。神经进化是指后一种情况，即在其中使用进化算法。神经进化方面的大多数工作都集中于一种特定类型的ANN，即卷积神经网络（CNN）。此外，这些工作大多采用了单一的优化方法。本项工作在用于车辆轨迹预测（称为神经轨迹预测）的神经进化方面迈出了渐进的一步，其中必须考虑多个目标。为此，采用了由CNN和长短期记忆网络组成的丰富ANN。还采用了两种著名且稳健的进化多目标优化（EMO）算法，即NSGA-II和MOEA/D。每种算法完全不同的底层机制揭示了在神经轨迹预测中使用一种EMO方法相较于另一种方法的含义。特别地，强调了考虑目标缩放的重要性，发现MOEA/D可能更擅长关注特定目标，而NSGA-II往往对目标缩放更具不变性。此外，某些目标被发现要么有利于寻找有效模型，要么会对其产生不利影响，例如，包含距离反馈目标对寻找有效模型相当不利，而横向速度目标则更为有利。