Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. We propose a linear driver model, which can calculate node points that reflect the preferences of human drivers and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature. We apply this model to a self-developed Euler-curve-based curve fitting algorithm. Through a case study, we show that the model based planned path can reproduce the average behavior of human curve path selection. We analyze the performance of the proposed model through statistical analysis that shows the validity of the captured relations.

翻译：自动驾驶系统常被用于车道保持任务。此类系统会在车辆前方规划局部路径，但人类驾驶员常认为这些路径不自然。我们提出一种线性驾驶员模型，该模型能够计算反映人类驾驶员偏好的节点点，并基于这些节点点为自动驾驶设计符合人类偏好的运动路径。模型输入为道路曲率。我们将该模型应用于自主研发的基于欧拉曲线的曲线拟合算法。通过案例研究表明，基于该模型规划的路径可复现人类曲线路径选择的平均行为。我们通过统计分析验证了所提出模型的性能，证实了所捕获关系的有效性。