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Nowadays, vehicles are the most important means of transportation in our daily lifes. During the last few decades, many studies have been carried out in the field of intelligent vehicles and significant results on the behavior of car-following and lane-change maneuvers have been achieved. However, the effects of lane-change on the car-following models have been relatively neglected. This effect is a temporary state in car-following behavior during which the follower vehicle considerably deviates from conventional car-following models for a limited time. This paper aims to investigate the behavior of the immediate follower during the lane-change of its leader vehicle. Based on a closer inspection of the microstructure behavior of real drivers, this temporary state is divided into two stages of anticipation and evaluation. Afterwards, a novel and adaptive neuro-fuzzy model that considers human driving factors is proposed to simulate the behavior of real drivers. Comparison between model results and real traffic data reveals that the proposed model can describe anticipation and evaluation behavior with smaller errors. The anticipation and evaluation model can modify current car-following models so as to accurately simulate the behavior of an immediate follower which leads to an enhancement of carfollowing applications such as driving assistance and collision avoidance systems.

Car-following and lane-changing manoeuvres are the most common driving behaviour on urban roads and highways. Although these two manoeuvres have been studied extensively, the effect of a lane change on a car-following manoeuvre remains elusive. Analysing these effects leads to integration of the car-following and the lane-changing manoeuvre which has been relatively neglected. A lane-changing manoeuvre causes the immediately following driver to deviate from common car-following models to accommodate the lane changer ahead; this is called anticipation and relaxation behaviour. These behaviours are transient states which occur between two car-following behaviours owing to the lane-changing manoeuvre. In this paper, a novel adaptive neurofuzzy model is proposed for simulating the behaviour of the follower vehicle during anticipation and relaxation behaviour. Comparison between the simulation results and the field data shows that errors in the proposed model are significantly smaller and the model can describe anticipation and relaxation behaviour properly. The anticipation and relaxation model can improve current car-following model applications to enhance the safety of vehicles such as driving assistant and collision avoidance systems.

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.

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