Abstract
Human drivers’ behavior, which is very difficult to model, is a very complicated stochastic system. To characterize a high-accuracy driver behavior model under different roadway geometries, the paper proposes a new algorithm of driver behavior model based on the whale optimization algorithm-restricted Boltzmann machine (WOA-RBM) method. This method establishes an objective optimization function first, which contains the training of RBM deep learning network based on the real driver behavior data. Second, the optimal training parameters of the restricted Boltzmann machine (RBM) can be obtained through the whale optimization algorithm. Finally, the well-trained model can be used to represent the human drivers’ operation effectively. The MATLAB simulation results showed that the driver model can achieve an accuracy of 90%.
Highlights
Driver models can be applied to (1) vehicle dynamics [1] including vehicle component design, vehicle dynamics analysis, overall vehicle stability analysis, and design of onboard controls; (2) intelligent transport systems (ITS) [2, 3] including simulation of traffic flow based on the control theory models of driver behavior and modeling drivers’ risk taking behavior; (3) driverless vehicle systems [4]; and (4) traffic energy consumption systems [5]
Traffic energy consumption systems are different from the vehicle dynamics simulation. e traffic energy consumption system will be affected by the road, so our research focuses on how the road grade impacts on the driver’s behavior characteristics
Designing a drive cycle of the vehicle requires investigating and collecting the practical driving data, analyzing the experimental data, and establishing the road vehicle driving conditions using relevant mathematical theoretical methods. e vehicle speed of this paper is collected based on the distance, and we considered how the road grade influences driving speeds in its operating conditions. e operating conditions of the resulting vehicles can be used to determine the vehicle’s fuel consumption and the technical development as well as evaluation of new models
Summary
Driver models can be applied to (1) vehicle dynamics [1] including vehicle component design, vehicle dynamics analysis, overall vehicle stability analysis, and design of onboard controls; (2) intelligent transport systems (ITS) [2, 3] including simulation of traffic flow based on the control theory models of driver behavior and modeling drivers’ risk taking behavior; (3) driverless vehicle systems [4]; and (4) traffic energy consumption systems [5]. Yamada and Takahashi [22] proposed a driver behavior modeling method based on real traffic data under varying environmental conditions. In this method, the driving speed is assumed to be a function of several factors such as overall traveling schedule, speed, and road surface conditions. E establishment of methods in [22,23,24, 29] requires a large amount of actual driving data as measurement data, which is strongly dependent on historical data To solve these problems, the paper presents a new method of driver behavior model based on WOA-RBM.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
