Implementation of Human-Like Driver Model Based on Recurrent Neural Networks

Li, Aoxue; Jiang, Haobin; Zhou, Jie; Zhou, Xinchen

doi:10.1109/access.2019.2930873

Cited by 19 publications

(10 citation statements)

References 43 publications

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“…So, 10 taxi drivers, 5 bus drivers, and 5 driving instructors are recruited as the representatives of experienced drivers. 28 Table 1 illustrates the average age, gender, driving experience, and annual vehicle kilometers traveled (AVKT) of all the participants. Among the participants, 4 are females and 16 are males, and the average age is about 41.7.…”

Section: Participantsmentioning

confidence: 99%

“…The above considerations ensure that the collected vehicle speeds, trajectories, and steering wheel angles are the real driving data of experienced drivers under normal conditions. Li et al 28,30 illustrated the detailed process of the experiment.…”

Section: Participantsmentioning

confidence: 99%

“…A measurement steering wheel (MSW) is mounted on the steering wheel of the original vehicle. 28 The vehicle speeds, trajectories, steering wheel angles, and sampling time are collected through the apparatus. The acquisition accuracy of the DGPS/INS is +0.01 m; the type of MSW is Kistler CMSWB, the measurement range of steering angle is +1250 , and the accuracy of MSW is +0.1 .…”

Section: Experimental Car and Apparatusmentioning

confidence: 99%

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Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Jiang

Zhou

et al. 2020

International Journal of Advanced Robotic Systems

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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.

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Section: Participantsmentioning

confidence: 99%

Section: Participantsmentioning

confidence: 99%

Section: Experimental Car and Apparatusmentioning

confidence: 99%

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Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Jiang

Zhou

et al. 2020

International Journal of Advanced Robotic Systems

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“…Okamoto and Panagiotis [9] studied the performance of some data-driven human driver models that are able to predict the torque applied by the driver on the steering wheel. In [10,11], driver models were developed that imposed the steering wheel angle using a neural network.…”

Section: Introductionmentioning

confidence: 99%

Study on the Driver/Steering Wheel Interaction in Emergency Situations

Comolli¹,

Gobbi

Mastinu

2020

Applied Sciences

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Advanced driver assistance systems (ADAS) are becoming increasingly prevalent. The tuning of these systems would benefit from a deep knowledge of human behaviour, especially during emergency manoeuvres; however, this does not appear to commonly be the case. We introduced an instrumented steering wheel (ISW) to measure three components of force and three components of the moment applied by each hand, separately. Using the ISW, we studied the kick plate manoeuvre. The kick plate manoeuvre is an emergency manoeuvre to recover a lateral disturbance inducing a spin. The drivers performed the manoeuvre either keeping two hands on the steering wheel or one hand only. In both cases, a few instants after the lateral disturbance induced by the kick plate occurred, a torque peak was applied at the ISW. Such a torque appeared to be unintentional. The voluntary torque on the ISW occurred after the unintentional torque. The emergency manoeuvre performed with only one hand was quicker, since, if two hands were used, an initial fighting of the two hands against each other was present. Therefore, we propose to model the neuro-muscular activity in driver models to consider the involuntary muscular phenomena, which has a relevant effect on the vehicle dynamic response.

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“…To make the traffic flow more harmonious, it is good to let AVs obtain human drivers' steering characteristic, which could release the mental pressure of human drivers dealing with surrounding AVs. In addition, driver models developed for AVs are mostly concentrate on tracking accuracy and controlling stability, however humanlike driver model, especially human-like steering control driver model is rarely researched [2].…”

Section: Introductionmentioning

confidence: 99%

A New Single Point Preview-Based Human-Like Driver Model on Urban Curved Roads

Zhou

Jiang

et al. 2020

IEEE Access

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To make intelligent vehicles obtain human drivers' steering characteristic, a new single point preview-based human-like driver model is proposed, which contains a preview decision module and a steering wheel angle calculation module. Enlightened by the visual gaze mechanism of human drivers when passing a curved road, the preview decision module is established using Takagi-Sugeno fuzzy inference system (T-S FIS) to adaptively adjust the preview point position in both longitudinal and lateral direction, and the steering angle calculation module would use the adjusted preview point to generate steering command based on pure pursuit algorithm. Ant colony optimization (ACO) method is used to optimize the fuzzy rules in the preview decision module according to the similarity of trajectories between the proposed driver model and human drivers. The proposed human-like driver model is verified on a two-lane urban curved road. Five experienced human drivers' driving trajectories under different speeds are collected for the verification. After the preview decision module optimization done in the PreScan/Simulink simulation platform, the proposed human-like driver model shows higher similarity with experienced human drivers than the driver model with fixed preview distance or the driver model only with changeable longitudinal preview distance.

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Implementation of Human-Like Driver Model Based on Recurrent Neural Networks

Cited by 19 publications

References 43 publications

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

Study on the Driver/Steering Wheel Interaction in Emergency Situations

A New Single Point Preview-Based Human-Like Driver Model on Urban Curved Roads

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