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Decision Tree-Based Maneuver Prediction for Driver Rear-End Risk-Avoidance Behaviors in Cut-In Scenarios
作者 Manjiang Hu, Yuan Liao, Wenjun Wang, Guofa Li, Bo Cheng, and Fang Chen
出版年份 2017 引用
摘要
Predicting driver rear-end risk-avoidance maneuvers in cut-in scenarios, especially dangerous precrash scenarios, benefits the customization of automatic driving, particularly automatic steering. This paper studies driver rear-end ris...展开全文
Predicting driver rear-end risk-avoidance maneuvers in cut-in scenarios, especially dangerous precrash scenarios, benefits the customization of automatic driving, particularly automatic steering. This paper studies driver rear-end risk-avoidance behaviors in cut-in scenarios on a straight three-lane highway. Data from 24 participants in 1326 valid trials were collected using a motion-based driving simulator. An Eysenck Personality Questionnaire (revised for Chinese participants) was used to obtain the personality traits of the participants. Based on a statistical analysis, the candidate features used in the driver maneuver prediction were determined as a combination of objective risk indicators and driver characteristics. A decision tree-based model was constructed for maneuver prediction in cut-in scenarios. The prediction accuracy of the extracted classification rules was 79.2% for the training data set and 80.3% for the test data set. The most powerful predictive variables were extracted, and their effects on maneuver decisions were analyzed. The results show that driver characteristics strongly influence the prediction of maneuver decisions.收起全文

Energy-Efficient Torque Allocations for a Two-Axle Four-Wheel-Drive Electric Bus
作者 admin
出版年份 2017 引用
摘要
Optimal torque distribution approaches are introduced for a two-axle dual-motor four-wheel-drive (4WD) electric bus (E-Bus) developed by our team to improve vehicle economical performance. Real-time vehicle speed and demand ...展开全文
Optimal torque distribution approaches are introduced for a two-axle dual-motor four-wheel-drive (4WD) electric bus (E-Bus) developed by our team to improve vehicle economical performance. Real-time vehicle speed and demand torque are taken as the input of these strategies. The objective function is formulated based on the practical energy consumption in both driving and regenerative braking conditions according to the efficiency map of each motor. Two torque distribution strategies-off-line calculation with on-line look-up-table strategy and the fuzzy logic based torque distribution strategy are proposed. Co-simulations with Simulink and TruckSim demonstrate the effectiveness of the two distribution strategies. The comparison with original uniform distribution strategy is also carried out to illustrate the effectiveness on reducing vehicle energy consumption of the two torque distribution strategies. The mileage of E-bus can be improved without extra cost through optimal torque distribution strategies. 收起全文

Energy-Efficient Torque Allocations for a Two-Axle Four-Wheel-Drive Electric Bus
作者 Zeyu Yang, Zhanyi Hu, ManjiangHu, Zhihua, Zhong
出版年份 2017 引用
摘要
Optimal torque distribution approaches are introduced for a two-axle dual-motor four-wheel-drive (4WD) electric bus (E-Bus) developed by our team to improve vehicle economical performance. Real-time vehicle speed and demand ...展开全文
Optimal torque distribution approaches are introduced for a two-axle dual-motor four-wheel-drive (4WD) electric bus (E-Bus) developed by our team to improve vehicle economical performance. Real-time vehicle speed and demand torque are taken as the input of these strategies. The objective function is formulated based on the practical energy consumption in both driving and regenerative braking conditions according to the efficiency map of each motor. Two torque distribution strategies-off-line calculation with on-line look-up-table strategy and the fuzzy logic based torque distribution strategy are proposed. Co-simulations with Simulink and TruckSim demonstrate the effectiveness of the two distribution strategies. The comparison with original uniform distribution strategy is also carried out to illustrate the effectiveness on reducing vehicle energy consumption of the two torque distribution strategies. The mileage of E-bus can be improved without extra cost through optimal torque distribution strategies.收起全文

V2I based cooperation between traffic signal and approaching automated vehicles
作者 Biao Xu,Xuegang(Jeff)Ban,Yougang Bian,Jianqiang Wang,Keqiang Li
出版年份 2017 引用
摘要
Existing traffic signal optimization and vehicle speed optimization at signalized intersections cannot work together for the lack of proper cooperation methods. We propose the V2I (vehicle to infra...展开全文
Existing traffic signal optimization and vehicle speed optimization at signalized intersections cannot work together for the lack of proper cooperation methods. We propose the V2I (vehicle to infrastructure) based cooperation between traffic signal and approaching vehicles which optimizes the traffic signal and vehicles' speed trajectories simultaneously. The cooperation consists of roadside traffic signal optimization and onboard speed control, of which the former calculates the optimal traffic signal timing and vehicles' arriving time to minimize trip time and the latter optimizes the vehicle engine power and brake force to minimize the fuel consumption in the whole trip. A simulation study is conducted to compare the proposed cooperation method and the actuated signal control method. The simulation results show significant improvement of transportation efficiency and vehicle fuel economy by using the cooperation method.
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Coordinated collision avoidance for connected vehicles using relative kinetic energy density
作者 Manjiang Hu,Jian Wu,Hongmao Qin,Yougang Bian,Biao Xu,Qing Xu,Jugang He,Jianqiang Wang
出版年份 2017 引用
摘要
Vehicular collision often leads to serious casualties and traffic congestion, and the consequences are worse for multiple-vehicle collision. Many previous works on collision avoidance have only focus...展开全文
Vehicular collision often leads to serious casualties and traffic congestion, and the consequences are worse for multiple-vehicle collision. Many previous works on collision avoidance have only focused on the case for two consecutive vehicles using on-board sensors, which ignored the influence on upstream traffic flow. This paper proposes a novel coordinated collision avoidance (CCA) strategy for connected vehicles, which has potential to avoid collision and smooth the braking behaviors of multiple vehicles, leading to an improvement of traffic smoothness. Specifically, model predictive control (MPC) framework is used to formulate the CCA into an optimization problem, where the objective is to minimize the total relative kinetic energy density (RKED) among connected vehicles. Monte Carlo simulations are used to demonstrate the effectiveness of proposed CCA strategy by comparison with other two strategies. Among all the three control strategies, the RKED based control strategy shows the best performance of collision avoidance, including the best crash prevention rates (99.2 % on dry asphalt road and 90.5 % on wet asphalt road) and the best control of distance headways between vehicles.
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Autonomous parking control for intelligent vehicles based on a novel algorithm
作者 Hongbo Gao, Guotao Xie,Xinyu Zhang,Bo Cheng
出版年份 2017 引用
摘要
Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver's driving technology. How to safely, quickly ...展开全文
Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver's driving technology. How to safely, quickly and accurately park the vehiclo to parking space right? This paper presents an automatic parking scheme based on trajectory planning, which analyzing the mechanical model of the vehicle, establishing vehicle steering model and parking model, coming to the conclusion that it is the turning radius is independent of the vehicle speed at low speed. The Matlab simulation environment verifies the correctness and effectiveness of the proposed algorithm for parking. A class of the automatic parking problem of intelligent vehicles is solved.
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Lateral control of autonomous vehicles based on learning driver behavior via cloud model
作者 Hongbo Gao,Guotao Xie,Hongzhe Liu,Xinyu Zhang,Deyi Li
出版年份 2017 引用
摘要
In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative o...展开全文
In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative of the lateral control of the intelligent vehicle. Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller: expectation, entropy and hyper entropy. In order to verify the effectiveness of the cloud controller, joint simulation platform based on Matlab/Simulink/CarSim is established. Experimental analysis shows that: driver's lateral controller based on cloud model is able to achieve tracking of the desired angle, and achieve good control effect, it also verifies that a series of mental activities such as feeling, cognition, calculation, decision and so on are fuzzy and uncertain.
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Modeling Discretionary Cut-in Risks Using Naturalistic Driving Data
作者 Guotao Xie, Hongmao Qin,Manjiang Hu,Daiheng Ni,Jianqiang Wang
出版年份 2017 引用
摘要
One of the operational issues that intelligent vehicles have to deal with is cut-into and by other vehicles. A vehicle cut-in risk model helps determine how an intelligent vehicle should react to th...展开全文
One of the operational issues that intelligent vehicles have to deal with is cut-into and by other vehicles. A vehicle cut-in risk model helps determine how an intelligent vehicle should react to the other vehicle’s cut-in behavior. On the other hand, such a model could also help intelligent vehicles carry out cut-in maneuver in a considerate manner to minimize the impact on following vehicles in the target lane. In this study, a discretionary cut-in risk model for vehicles is developed on the basis of field driving data and machine learning methods, namely, decision trees and Support Vector Machine (SVM). A united algorithm is developed to combine the two machine learning models for achieving enhanced conservativeness to the traffic states with high misclassification costs. To build the naturalistic driving database, the wavelet method is employed for filtering; the K-means approach, an unsupervised data learning method, is used to categorize the cut-in impact on the following vehicles in the target lane into three groups. The impact is indicated by the following vehicle’s average and maximum deceleration. Using this model, intelligent vehicles can assess the risk level during other vehicles’ cut-in process as well as their own impact on the following vehicle in the target lane when carrying out cut-in maneuver.
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