The selection of the target variable is important while learning parameters of the classical car following models like GIPPS, IDM, etc. There is a vast body of literature on which target variable is optimal for classical car following models, but there is no study that empirically evaluates the selection of optimal target variables for black-box models, such as LSTM, etc. The black-box models, like LSTM and Gaussian Process (GP) are increasingly being used to model car following behavior without wise selection of target variables. The current work tests different target variables, like acceleration, velocity, and headway, for three black-box models, i.e., GP, LSTM, and Kernel Ridge Regression. These models have different objective functions and work in different vector spaces, e.g., GP works in function space, and LSTM works in parameter space. The experiments show that the optimal target variable recommendations for black-box models differ from classical car following models depending on the objective function and the vector space. It is worth mentioning that models and datasets used during evaluation are diverse in nature: the datasets contained both automated and human-driven vehicle trajectories; the black-box models belong to both parametric and non-parametric classes of models. This diversity is important during the analysis of variance, wherein we try to find the interaction between datasets, models, and target variables. It is shown that the models and target variables interact and recommended target variables don't depend on the dataset under consideration.

翻译：目标变量的选取对于经典跟驰模型（如GIPPS、IDM等）的参数学习至关重要。已有大量文献探讨经典跟驰模型的最优目标变量，但尚无研究系统评估黑箱模型（如LSTM等）的最优目标变量选择方法。当前，黑箱模型（如LSTM和高斯过程GP）在未对目标变量进行审慎选择的情况下，正被广泛用于跟驰行为建模。本研究针对三种黑箱模型——GP、LSTM和核岭回归，测试了加速度、速度、车头时距等不同目标变量。这些模型具有不同的目标函数，并在不同的向量空间中运作：例如GP在函数空间工作，LSTM在参数空间工作。实验表明，黑箱模型的最优目标变量推荐方案因目标函数和向量空间的不同，与经典跟驰模型存在差异。值得注意的是，评估中使用的模型和数据集具有多样性：数据集包含自动驾驶和人类驾驶车辆轨迹；黑箱模型涵盖参数模型与非参数模型两类。这种多样性对方差分析至关重要——我们试图在此过程中探究数据集、模型与目标变量之间的交互作用。结果表明，模型与目标变量之间存在交互效应，且推荐的目标变量不依赖于所考虑的数据集。