Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel predictio...Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.展开更多
Based on wind-speed records of Alaska’s 19 first-order weather stations, we analyzed the near-surface wind-speed stilling for January 1, 1984 to December 31, 2016. With exception of Big Delta that indicates an increa...Based on wind-speed records of Alaska’s 19 first-order weather stations, we analyzed the near-surface wind-speed stilling for January 1, 1984 to December 31, 2016. With exception of Big Delta that indicates an increase of 0.0157 m·s–1·a–1, on average, all other first-order weather stations show declining trends in the near-surface wind speeds. In most cases, the average trends are less then?–0.0300?m·s–1·a–1. The strongest average trend of?–0.0500?m·s–1·a–1 occurred at Homer, followed by?–0.0492?m·s–1·a–1 at Bettles, and?–0.0453?m·s–1·a–1 at Yakutat, while the declining trend at Barrow is marginal. The impact of the near-surface wind-speed stilling on the wind-power potential expressed by the wind-power density was predicted and compared with the wind-power classification of the National Renewable Energy Laboratory and the Alaska Energy Authority. This wind-power potential is, however, of subordinate importance because wind turbines only extract a fraction of the kinetic energy from the wind field characterized by the power efficiency. Since wind turbine technology has notably improved during the past 35 years, we hypothetically used seven currently available wind turbines of different rated power and three different shear exponents to assess the wind-power sustainability under changing wind regimes. The shear exponents 1/10, 1/7, and 1/5 served to examine the range of wind power for various conditions of thermal stratification. Based on our analysis for January 1, 1984 to December 31, 2016, Cold Bay, St. Paul Island, Kotzebue, and Bethel would be very good candidates for wind farms. To quantify the impact of a changing wind regime on wind-power sustainability, we predicted wind power for the periods January 1, 1984 to December 31, 1994 and January 1, 2006 to December 31, 2016 as well. Besides Big Delta that suggests an increase in wind power of up to 12% for 1/7, predicted wind power decreased at all sites with the highest decline at Annette (≈38%), Kodiak (≈30%), King Salmon (≈26%), and Kotzebue (≈24%), where the effect of the shear exponents was marginal. Bethel (up to 20%) and Cold Bay (up to 14%) also show remarkable decreases in predicted wind power.展开更多
Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the...Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.展开更多
The paper discusses the characteristics of next generation services, and analyzes its development barriers and cut-in points.Broadband services that NGN offers have to transit from an unknown state to an popular state...The paper discusses the characteristics of next generation services, and analyzes its development barriers and cut-in points.Broadband services that NGN offers have to transit from an unknown state to an popular state to become a scale economy.Personal information and communication services and multimedia entertainment services will be the cut-in points.展开更多
Commercially available wind-turbines are optimized to operate at certain wind velocity, known as rated wind velocity. For other values of wind velocity, it has different output which is lower than the rated output of ...Commercially available wind-turbines are optimized to operate at certain wind velocity, known as rated wind velocity. For other values of wind velocity, it has different output which is lower than the rated output of the wind plant. Wind mill can be designed to provide maximum power output at different wind velocities through modification of swept area to match with the wind speed available at the moment. This can result in higher power output at all the velocities except that at rated wind speed because of limitation of generator. This results in increased utilization of generation capacity of wind mill compared to its commercially designed counterpart. A theoretical simulation has been done to prove a new concept about swept area of wind turbine blade which results in a significant increase in the power output through the year. Simulation results of power extracted through normal wind blade design and new concept are studied and compared. The findings of the study are presented in graphical and tabular form. Study establishes that there can be a significant gain in the power output with the new concept.展开更多
High efficiency Double-Fed Induction Generator applies new power electronic technology, and utilizes vector control to fix the magnetic direction of the stator to the vertical axis. Adjusting the input current of roto...High efficiency Double-Fed Induction Generator applies new power electronic technology, and utilizes vector control to fix the magnetic direction of the stator to the vertical axis. Adjusting the input current of rotor via an inverter can separately control the cross axis and vertical axis current of real power and reactive power of a generator. Traditionally, rotating speed affects frequency and the output is unstable. This study concentrates on high efficiency Double-Fed Induction Generators and Traditional Generators from mathematic model to derive and control the characteristics simulation and comparison than get an output of high efficiency Double-Fed Industrial Generators. This study utilizes the simulation software MATLAB/Simulink to simulate the response characteristics of vector control of a Double-Fed Industrial Generator. The operating and control functions are better than those of a traditional generator.展开更多
This study investigated the uncertainty assessing wind-power production in valleys of complex terrain using Juneau, Alaska as the testbed. The wind-speed data stem from evaluated WRF/Chem simulations for seven tourist...This study investigated the uncertainty assessing wind-power production in valleys of complex terrain using Juneau, Alaska as the testbed. The wind-speed data stem from evaluated WRF/Chem simulations for seven tourist seasons (May 15 to September 15). The percentage of wind speeds between cut-in and cutout speed differed up to about 11% among tourist seasons and up to 15% among the examined wind-turbine types. The wind-speed probability density varied the strongest among tourist seasons for wind speeds less than 3 m·sǃ (6 m·sǃ) for wind turbines with hub heights of about 80 m (30 m). At these heights, the interannual differences in the probability density of wind speeds at the rated or higher power were about half or less than those at wind speeds below 3 m·sǃ (6 m·sǃ). The predicted average power output notably differed among tourist seasons. The tall (small) turbines had their highest predicted average production in 2006 (2012). The ranking among wind turbines regarding the predicted average power production was independent of the interannual variability in average power production. Capacity factors differed about 8% (6%) for the tall (small) tubines among tourist seasons. Within the same tourist season, capacity factors differed about 8% (5%) among turbine types. Estimates of capacity and potential power derived from 10 m wind-speed observations by an empirical formula commonly used to estimate wind speeds at hub height, differed up to 40% for 80 m height for some turbine types. Determinating the exponent of the empirical equation by means of WRF/Chem data showed that the traditional empirical approach failed in complex terrain.展开更多
Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence.While the UN R157 Regulation evaluates automated lane-keeping system(...Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence.While the UN R157 Regulation evaluates automated lane-keeping system(ALKS)performance baselines through safe collision plots(SCPs)in various scenario clusters,quantifying the specific ALKS safety efficiency remains challenging.We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios.First,we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process.Second,by utilizing Fourier analysis,a spectral analysis model was built to quantify and analyze the vehicle motion characteristics,providing insights into scenario safety.Finally,we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model.The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2.When the relative longitudinal distance and speed of the vehicle are the same,if the cut-in speed of the cut-in vehicle is larger,the normalized disturbance frequency is higher,indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.展开更多
With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-fie...With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example,based on an autoencoder and a generative adversarial network(GAN),a method that combines Transformer to capture the features of a long-time series,called SceGAN,is proposed to model and generate scenarios of autonomous vehicles on highways.An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage.Experiments showed that compared with TimeGAN and AEGAN,SceGAN is superior in data fidelity and availability,and their similarity increased by 27.22%and 21.39%,respectively.The coverage increased from 79.84%to 93.98%as generated scenarios increased from 2,547 to 50,000,indicating that the proposed method has a strong generalization capability for generating multiple trajectories,providing a basis for generating test scenarios and promoting autonomous vehicle testing.展开更多
To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in...To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations.First,the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set.Then,a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario,and the performance challenge between the driver and the vehicle is established.Next,the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function.Finally,the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library.Simulation results show that the proposed method for scenario library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.展开更多
The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban expressways.Risk prediction and characteristics analysis of cut-ins are part of the es...The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban expressways.Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences.This paper makes some efforts on these purposes.In this paper,twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection.The surrogate measures,Time Exposure Time-to-Collision(TET)and Time Integrated Time-to-collision(TIT)were employed to quantify the risk of cut-ins,then k-means clustering was applied for risk classification of 3 levels.Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables.Based on these variables,three prediction models including decision tree(DT),gradient boosting decision tree(GBDT)and long shortterm memory(LSTM)are used for predicting the risks of cut-ins.Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data.From results of risk prediction models,the LSTM,with an overall accuracy of 87%,outperforms the GBDT(80.67%)and DT(76.9%).Despite this,this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.展开更多
基金This study was supported by the Research and Application of Key Technologies in the Design of Large Onshore Smart Wind Power Base(Grant No.XBY-ZDKJ-2020-05)the Scientific Research Project of the China Electric Power Construction Corporation:Research and Application of Key Technologies in the Design of an Onshore Smart Wind Power Base(Grant No.DJ-ZDXM-2020-52)+2 种基金the Danish Energy Agency(Grant No.64013-0405)the Fundamental Research Funds for the Central Universities(Grant No.B210201018)the Jiangsu Province Policy Guidance Program(Grant No.BZ2021019).
文摘Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms.
文摘Based on wind-speed records of Alaska’s 19 first-order weather stations, we analyzed the near-surface wind-speed stilling for January 1, 1984 to December 31, 2016. With exception of Big Delta that indicates an increase of 0.0157 m·s–1·a–1, on average, all other first-order weather stations show declining trends in the near-surface wind speeds. In most cases, the average trends are less then?–0.0300?m·s–1·a–1. The strongest average trend of?–0.0500?m·s–1·a–1 occurred at Homer, followed by?–0.0492?m·s–1·a–1 at Bettles, and?–0.0453?m·s–1·a–1 at Yakutat, while the declining trend at Barrow is marginal. The impact of the near-surface wind-speed stilling on the wind-power potential expressed by the wind-power density was predicted and compared with the wind-power classification of the National Renewable Energy Laboratory and the Alaska Energy Authority. This wind-power potential is, however, of subordinate importance because wind turbines only extract a fraction of the kinetic energy from the wind field characterized by the power efficiency. Since wind turbine technology has notably improved during the past 35 years, we hypothetically used seven currently available wind turbines of different rated power and three different shear exponents to assess the wind-power sustainability under changing wind regimes. The shear exponents 1/10, 1/7, and 1/5 served to examine the range of wind power for various conditions of thermal stratification. Based on our analysis for January 1, 1984 to December 31, 2016, Cold Bay, St. Paul Island, Kotzebue, and Bethel would be very good candidates for wind farms. To quantify the impact of a changing wind regime on wind-power sustainability, we predicted wind power for the periods January 1, 1984 to December 31, 1994 and January 1, 2006 to December 31, 2016 as well. Besides Big Delta that suggests an increase in wind power of up to 12% for 1/7, predicted wind power decreased at all sites with the highest decline at Annette (≈38%), Kodiak (≈30%), King Salmon (≈26%), and Kotzebue (≈24%), where the effect of the shear exponents was marginal. Bethel (up to 20%) and Cold Bay (up to 14%) also show remarkable decreases in predicted wind power.
基金supported in part by the Key-Area Researchand Development Program of Guangdong Province(2020B0909050003)the Program of Jiangxi(20204ABC03A13)。
文摘Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by cut-in.To improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic participants.The integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and trajectory.The dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference performance.Based on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax function.Finally,the comparative analysis is performed to validate the proposed method using the naturalistic driving data.The results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.
文摘The paper discusses the characteristics of next generation services, and analyzes its development barriers and cut-in points.Broadband services that NGN offers have to transit from an unknown state to an popular state to become a scale economy.Personal information and communication services and multimedia entertainment services will be the cut-in points.
文摘Commercially available wind-turbines are optimized to operate at certain wind velocity, known as rated wind velocity. For other values of wind velocity, it has different output which is lower than the rated output of the wind plant. Wind mill can be designed to provide maximum power output at different wind velocities through modification of swept area to match with the wind speed available at the moment. This can result in higher power output at all the velocities except that at rated wind speed because of limitation of generator. This results in increased utilization of generation capacity of wind mill compared to its commercially designed counterpart. A theoretical simulation has been done to prove a new concept about swept area of wind turbine blade which results in a significant increase in the power output through the year. Simulation results of power extracted through normal wind blade design and new concept are studied and compared. The findings of the study are presented in graphical and tabular form. Study establishes that there can be a significant gain in the power output with the new concept.
文摘High efficiency Double-Fed Induction Generator applies new power electronic technology, and utilizes vector control to fix the magnetic direction of the stator to the vertical axis. Adjusting the input current of rotor via an inverter can separately control the cross axis and vertical axis current of real power and reactive power of a generator. Traditionally, rotating speed affects frequency and the output is unstable. This study concentrates on high efficiency Double-Fed Induction Generators and Traditional Generators from mathematic model to derive and control the characteristics simulation and comparison than get an output of high efficiency Double-Fed Industrial Generators. This study utilizes the simulation software MATLAB/Simulink to simulate the response characteristics of vector control of a Double-Fed Industrial Generator. The operating and control functions are better than those of a traditional generator.
文摘This study investigated the uncertainty assessing wind-power production in valleys of complex terrain using Juneau, Alaska as the testbed. The wind-speed data stem from evaluated WRF/Chem simulations for seven tourist seasons (May 15 to September 15). The percentage of wind speeds between cut-in and cutout speed differed up to about 11% among tourist seasons and up to 15% among the examined wind-turbine types. The wind-speed probability density varied the strongest among tourist seasons for wind speeds less than 3 m·sǃ (6 m·sǃ) for wind turbines with hub heights of about 80 m (30 m). At these heights, the interannual differences in the probability density of wind speeds at the rated or higher power were about half or less than those at wind speeds below 3 m·sǃ (6 m·sǃ). The predicted average power output notably differed among tourist seasons. The tall (small) turbines had their highest predicted average production in 2006 (2012). The ranking among wind turbines regarding the predicted average power production was independent of the interannual variability in average power production. Capacity factors differed about 8% (6%) for the tall (small) tubines among tourist seasons. Within the same tourist season, capacity factors differed about 8% (5%) among turbine types. Estimates of capacity and potential power derived from 10 m wind-speed observations by an empirical formula commonly used to estimate wind speeds at hub height, differed up to 40% for 80 m height for some turbine types. Determinating the exponent of the empirical equation by means of WRF/Chem data showed that the traditional empirical approach failed in complex terrain.
基金the National Key R&D Program of China(Grant No.2021YFB1600403)the National Natural Science Foundation of China(Grant Nos.51805312 and 52172388).
文摘Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence.While the UN R157 Regulation evaluates automated lane-keeping system(ALKS)performance baselines through safe collision plots(SCPs)in various scenario clusters,quantifying the specific ALKS safety efficiency remains challenging.We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios.First,we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process.Second,by utilizing Fourier analysis,a spectral analysis model was built to quantify and analyze the vehicle motion characteristics,providing insights into scenario safety.Finally,we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model.The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2.When the relative longitudinal distance and speed of the vehicle are the same,if the cut-in speed of the cut-in vehicle is larger,the normalized disturbance frequency is higher,indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.
基金supported by the National Key R&D Program of China(2021YFB2501200)the National Natural Science Foundation of China(52131204)the Shaanxi Province Key Research and Development Program(2022GY-300).
文摘With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example,based on an autoencoder and a generative adversarial network(GAN),a method that combines Transformer to capture the features of a long-time series,called SceGAN,is proposed to model and generate scenarios of autonomous vehicles on highways.An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage.Experiments showed that compared with TimeGAN and AEGAN,SceGAN is superior in data fidelity and availability,and their similarity increased by 27.22%and 21.39%,respectively.The coverage increased from 79.84%to 93.98%as generated scenarios increased from 2,547 to 50,000,indicating that the proposed method has a strong generalization capability for generating multiple trajectories,providing a basis for generating test scenarios and promoting autonomous vehicle testing.
基金Major Project of Scientific and Technological Innovation 2030“New Generation Artificial Intelligence”(Grant No.2020AAA0108105)National Nature Science Foundation of China(Grants Nos.62103162 and U19A2069)+1 种基金Jilin Key Research and Development Program(Grant No.20200401088GX)the Jilin Major Science and Technology Projects(Grant No.20200501011GX).
文摘To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations.First,the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set.Then,a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario,and the performance challenge between the driver and the vehicle is established.Next,the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function.Finally,the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library.Simulation results show that the proposed method for scenario library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.
基金Funding for this study was provided in part by the National Key R&D Program of China(2019YFB1600703)the Shanghai Sailing Program(20YF1451800).
文摘The cut-ins(one kind of lane-changing behaviors)have result in severe safety issues,especially at the entrances and exits of urban expressways.Risk prediction and characteristics analysis of cut-ins are part of the essential research for advanced in-vehicle technologies which can reduce crash occurrences.This paper makes some efforts on these purposes.In this paper,twenty-four participants were recruited to conduct the experiments of multi-driver simulation for risky driving data collection.The surrogate measures,Time Exposure Time-to-Collision(TET)and Time Integrated Time-to-collision(TIT)were employed to quantify the risk of cut-ins,then k-means clustering was applied for risk classification of 3 levels.Multiple candidate variables of two kinds were extracted including 10 behavioral variables and 7 driver trait variables.Based on these variables,three prediction models including decision tree(DT),gradient boosting decision tree(GBDT)and long shortterm memory(LSTM)are used for predicting the risks of cut-ins.Results from data validity verification show that the data collected from multi-driver simulation experiments is valid compared with real-world data.From results of risk prediction models,the LSTM,with an overall accuracy of 87%,outperforms the GBDT(80.67%)and DT(76.9%).Despite this,this paper also concludes the merits of the DT over the GBDT and LSTM in variable explanation and the results of DT suggest that controlling the proper lane-changing gap and short duration of cut-ins can help reduce risks of cut-ins.
