Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

• Published in: IEEE transactions on vehicular technology Vol. 70; no. 9; pp. 8567 - 8577
• Main Authors: Tajdari, Farzam, Golgouneh, Alireza, Ghaffari, Ali, Khodayari, Alireza, Kamali, Ali, Hosseinkhani, Niloofar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Accelerometers; Adaptive systems; Artificial neural networks; Biological system modeling; Car following; car-following behavior; comfort; Computational modeling; Control systems design; Controllers; Correlation coefficients; Distance measurement; Driver behavior; Driving; Fuzzy control; Fuzzy logic; Human factors; Human-like fuzzy controller; Lane changing; lane-changing behavior; Merging; Passenger comfort; Prediction models; Predictive models; Robust control; Root-mean-square errors; Safety; Sensors; Vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2021.3099736

Despite the advances related to car-following and lane-changing behaviors, the influence of lane-changing on the car-following models, which results in a complex transient merging behavior, has not comprehensively been investigated. This paper presents a novel fuzzy controller based on a human factor to optimize the Follower Vehicle (FV) behavior subject to safety, comfort, and convenient traveled time in the complex behavior where the Lane Changer (LC) vehicle exits the temporary lane. The factor enables the controller to mimic the current driver behavior in terms of maximum pleasantness of drive. Accordingly, the data of real-life experiments were used to design the human-like fuzzy controller, to build a predictive model to suggest the appropriate acceleration, velocity, and travel distance. At best, the correlation coefficient of 0.93 and the Root Mean Square Error (RMSE) of 0.71 were achieved for modeling using the adaptive Neuro-Fuzzy Inference System (ANFIS) utilizing Gaussian function as a membership function. Furthermore, to evaluate the robustness of the controller to uncertainties and unknown disturbances for real-time driving experiments, a test-bed was fabricated to mount the feedback sensors, including vision, accelerometer, and distance measurement sensors. The results of running the controller in various driving scenarios showed 70% and 38% improvements in safety and ride comfort, respectively. The proposed intelligent controller is intended to be used for vehicle route guidance and on urban highways.