Abstract
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.
Highlights
With the development of artificial intelligence and 5G technology in the past 5 years, the market for autonomous vehicles is extremely hot, but there is still a relatively long way to reach real industrialization.[1,2] At present, the industry generally agrees that the technologies that can take the lead to carry out are all autonomous driving technologies in specific scenarios, such as robo taxi,[3] auto valet parking,[4] and so on
This study focused on exploring the characteristics of experienced drivers’ steering maneuvers on curved roads
One specifical point was that the participants were all experienced drivers, another specifical point was that four specified speeds were required during experiments
Summary
With the development of artificial intelligence and 5G technology in the past 5 years, the market for autonomous vehicles is extremely hot, but there is still a relatively long way to reach real industrialization.[1,2] At present, the industry generally agrees that the technologies that can take the lead to carry out are all autonomous driving technologies in specific scenarios, such as robo taxi,[3] auto valet parking,[4] and so on. Complex traffic scenarios and the unpredictable behavior of traffic participants.[5,6] Some scholars realize that studying human driving behaviors will help to bring a better riding experience to autonomous vehicles under mixed traffic scenes (autonomous vehicles, humandriven vehicles, and pedestrians sharing the road) and improve the safety factor of all the vehicles.[7,8]. Many scholars have studied the relationships between human drivers’ vehicle speed, steering wheel angle, trajectory, and external environment (e.g. other vehicles, weather, and lane markings) or internal factors (e.g. age, driving experience, physical and mental states). Kihan et al.[9] presented a test result conducted in the proving ground and concluded that the preview distance of expert drivers was longer than novices’ as the road curvature was increased, the overall steering input of novice was larger than expert drivers and novice drivers generally tended to maneuver more excessively and unsafely than expert drivers. Kang et al.[13] used virtual driving simulator to observe human drivers’ behavior and they found that: when the driver was driving on the curves, they reduced vehicle speed and acceleration; when the driver was driving on the transitional road, they would increase vehicle speed and acceleration
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