Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

中文翻译：

---

自动驾驶汽车梯形方向盘转角模型的建立与跟踪控制

人类驾驶员在弯曲道路上具有丰富多样的驾驶特性。找出有经验的驾驶员在弯道行驶时的特征量，并将其应用于自动驾驶汽车的转向控制是本文的研究目标。我们首先招募了 10 名出租车司机、5 名公交车司机和 5 名驾驶教练作为经验丰富的司机代表，对 6 条不同长度和曲率的弯道进行了实车现场实验。在处理收集到的 Frenet 坐标系中的行驶数据后，考虑到真实汽车转向系统的自由游隙，有趣的是观察到方向盘角度和坐标轴围成的形状是梯形。然后，我们定义了四个特征点、四个特征距离和一个特征方向盘角度，梯形方向盘角度（TSWA）模型是通过反向传播神经网络开发的，输入是四个特征点的车速，道路曲率，输出是特征距离和特征方向盘角度。TSWA 模型与经验丰富的驾驶员、模型预测控制和基于预览的驾驶员模型的比较表明，所提出的 TSWA 模型最能反映经验丰富的驾驶员的转向特征。更重要的是，TSWA模型简洁的表达和人性化的特性使得自动驾驶汽车的人性化转向控制变得容易。最后，在 Simulink 中建立了一个由非线性车辆模型和电动助力转向 (EPS) 系统组成的自动驾驶汽车，TSWA 模型产生的方向盘角度由 EPS 电机直接跟踪，