Driver model is the most basic and important model for moving direction control of autonomous vehicles and it has been extensively studied from the perspective of precision and robustness of control, driving safety, and riding comfort. However, human-like driver model is a rarely mentioned research issue. In this paper, we first establish three preview-based driver models in PreScan+Simulink and collect human drivers' steering data on four two-way and two-lane free curved roads with 20 experienced drivers and one experimental vehicle under four specified speeds. Then, the similarities between steering wheel angles of preview-based models and those of human drivers are compared through dynamic time warping (DTW). From the calculation results of DTW and analysis of human drivers' gaze positions, it shows that the preview-based models are hard to reflect the characteristics of human drivers' maneuver. To this end, we propose a human-like driver model based on the continuity of human drivers' steering wheel angles. The experienced drivers' steering wheel angels are modeled with three different kinds of multivariate multi-step recurrent neural networks (RNNs) and the inputs of models are historical speeds, historical road curvatures, future road curvatures, and historical steering wheel angles, as well as the outputs are future steering wheel angles. By comparing the three RNN-based driver models with different configuration structures and historical steps, it is found that the long short-term memory (LSTM) model has the best prediction performance in validation and testing period. In this way, a data-driven human-like driver model is developed to generate human-like steering wheel angles on curved roads.
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.
With the rapid development of automated vehicles, there is currently a significant amount of automated driving research. Giving automated vehicles capabilities similar to those of experienced drivers will allow them to share the road harmoniously with manned vehicles, especially on two-lane urban curves. To represent the steering behavior of experienced drivers, a series of curve feature distances are proposed, which is determined by multi-regression. These series of curve feature distances are used to generate a trapezoidal steering angle model which imitates the steering behavior of the experienced test drivers. To verify the feasibility and human-likeness of the proposed trapezoidal steering angle model, the model is used with constant vehicle speed to plan a human-like trajectory which is tracked using model predictive control. The simulation results show that the proposed trapezoidal steering angle model is human-like and could be used to give automated vehicles human-like driving capability when driving on two-lane curves.
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