scholarly journals Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

2019 ◽  
Vol 68 (5) ◽  
pp. 4377-4390 ◽  
Author(s):  
Yang Xing ◽  
Chen Lv ◽  
Huaji Wang ◽  
Hong Wang ◽  
Yunfeng Ai ◽  
...  
Keyword(s):  
Intelligent Vehicles ◽  
Lane Change ◽  
Intention Inference

Model Predictive Automatic Lane Change Control for Intelligent Vehicles

2020 ◽  
Author(s):  
Ren Meng ◽  
Wu Guangqiang ◽  
Chen Xunjie ◽  
Liu Xuyang
Keyword(s):  
Intelligent Vehicles ◽  
Lane Change

New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers

2017 ◽  
Vol 232 (7) ◽  
pp. 936-945 ◽  
Author(s):  
Ali Ghaffari ◽  
Alireza Khodayari ◽  
Ali Kamali ◽  
Farzam Tajdari ◽  
Niloofar Hosseinkhani
Keyword(s):  
Evaluation Model ◽  
Fuzzy Model ◽  
Intelligent Vehicles ◽  
Lane Change ◽  
Car Following ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Driving Assistance ◽  
Important Means ◽  
Two Stages

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 car-following applications such as driving assistance and collision avoidance systems.

scholarly journals Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110033
Author(s):  
Junnian Wang ◽  
Fei Teng ◽  
Jing Li ◽  
Liguo Zang ◽  
Tianxin Fan ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Control Algorithm ◽  
Lateral Displacement ◽  
Weight Coefficient ◽  
Intelligent Vehicles ◽  
Slip Angle ◽  
Lane Change ◽  
Predictive Controller ◽  
Adaptive Control Algorithm ◽  
Fuzzy Control Algorithm

In order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles was constructed. Based on the vehicle model and the model predictive control theory, the time-varying linear variable path vehicle predictive model was derived by combining soft constraint of the side slip angle. Combined with fuzzy control algorithm, the weight coefficient of the deviation of the lateral displacement was dynamically adjusted. Finally, the FMPC (model predictive controller based on fuzzy control) and MPC controller were compared and analyzed by co-simulation of CarSim and Simulink under different speeds. The simulation results show that the designed FMPC controller can track the lane change trajectory better, and the controller has better robustness when the vehicle changes lanes at different speeds.

scholarly journals Optimal lane change motion of intelligent vehicles based on extended adaptive pseudo-spectral method under uncertain vehicle mass

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770282 ◽  
Author(s):  
Fang Zeping ◽  
Duan Jianmin
Keyword(s):  
Optimal Control ◽  
Optimal Control Problem ◽  
Intelligent Vehicles ◽  
Lane Change ◽  
Uncertain Parameters ◽  
Vehicle Model ◽  
Vehicle Mass ◽  
Motion Problem ◽  
Pseudo Spectral Method ◽  
Pseudo Spectral

Lane change operation is widely used in numerous traffic scenarios; to minimize lane change time and avoid collisions with other vehicles required by intelligent vehicles, an optimal lane change motion is proposed for the shortest time free lane change and emergency obstacle avoidance lane change. First, two optimal lane change motion models are constructed based on a 3-degree-of-freedom dynamical vehicle model. Because of uncertain parameters in the vehicle model, the optimal lane change motion problem is an uncertain optimal control problem. Subsequently, targeted at nonlinearity and uncertain parameters, an extended adaptive pseudo-spectral method is also presented on the basis of approximate and numerical integration of parameter space. Finally, solving the optimal lane change motion problem, optimization results, control results based on the nonlinear model predictive control algorithm, and experimental results are shown under circumstances of certain vehicle mass and uncertain vehicle mass. As for uncertain parameters, optimization results are featured with robustness. Thus, uncertain parameters need not be measured, and optimization calculation need not be carried out in real time. The algorithm put forward here could be adopted to solve an uncertain optimal control problem. The optimal lane change motion can be extended to apply in more complex operations like merging, double-lane change, entering/exiting highways, or overtaking another vehicle.

Driver Lane-Change Intention Inference

2020 ◽  
pp. 193-233
Author(s):  
Yang Xing ◽  
Chen Lv ◽  
Dongpu Cao
Keyword(s):  
Lane Change ◽  
Intention Inference

Lane-Change Intention Inference based on RNN for Autonomous Driving on Highways

2021 ◽  
pp. 1-1
Author(s):  
Lin Li ◽  
Wanzhong Zhao ◽  
Can Xu ◽  
Chunyan Wang ◽  
Qingyun Chen ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Lane Change ◽  
Intention Inference

scholarly journals Study on the Prediction of Lane Change Intention of Intelligent Vehicles in the Network Environment

2021 ◽  
Vol 12 (1) ◽  
pp. 27
Author(s):  
Shuaishuai Liu ◽  
Di Tan ◽  
Shilin Hong ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Lane Change ◽  
Training Set ◽  
Feed Forward Neural Network ◽  
Test Set ◽  
Experiment Platform ◽  
Motion State

The prediction of lane change intention of vehicles is an important part of the decision planning and control systems of intelligent vehicles. In the dynamic and complex traffic environment, the behaviors of traffic participants interact and influence each other. In lane change prediction, it is necessary to study the predicted vehicle and surrounding vehicles as an interactive correlation system. Otherwise, great errors are made in the motion prediction. Based on this, the motion state of the predicted vehicle, the position relationship between the predicted vehicle and lane, as well as the motion state of vehicles around the predicted vehicle are considered systematically in this paper, and the prediction of lane change intention of vehicles is studied. The influence of the three above-mentioned factors on the prediction of lane change intention is analyzed in this paper. On the basis of screening the prediction features of lane change intention, the lane change intention of vehicles is predicted by a feed-forward neural network. The data collected by the virtual driving experiment platform are divided into a training set, a verification set, and a test set. The neural network parameters of vehicles’ lane change intentions are identified by a training set, and the effect of prediction is tested by a verification set and a test set. The results show that the accuracy of the prediction model is high. The model is compared with the model of common features at the present stage and the model based on a Support Vector Machine, and the results show that the accuracy of the prediction model proposed in this paper was improved by 6.4% and 2.8%, respectively, compared with the two models. Finally, the virtual driving experiment platform was used to predict the lane change intention of the front vehicle and the vehicle in the left adjacent lane. The results show that, based on the same model and input features, the lane change intention of the front vehicle and the vehicle in the left adjacent lane can be predicted by the model at 2.8 s and 3.4 s before the lane change, and the model is a certain generality for the prediction of lane change intention of adjacent vehicles.

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