Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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.

Development Barriers and Cut-in Points of Next Generation Services

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.

Spectrum quantification-based safety efficiency evaluation of autonomous vehicle under random cut-in scenarios

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.

Generation of a scenario library for testing driver-automation cooperation safety under cut-in working conditions

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.

SceGAN:A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

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.

凸规划的新算法

对一般凸目标函数和一般凸集约束的凸规划问题新解法进行探讨,它是线性规划一种新算法的扩展和改进.此算法的基本思想是在规划问题的可行域中由所建的一个切割面到另一个切割面的不断推进来求取最优的.文章对目标函数是二次的且约束是一般凸集和二次目标函数且约束是线性的情形,给出了更简单的算法.

High Efficiency Double–Fed Induction Generator Applied to Wind Power Generator Technical Analyses

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.

Theoretical Study of Wind Turbine Model with a New Concept on Swept Area

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.

浅谈斐济南德瑞瓦图Nadarivatu项目调速系统控制理论

水轮机调速系统系由水轮机控制设备(系统)和被控制系统组成的闭环系统,是水电站水轮发电机组的重要辅助设备,它与电站二次回路或计算机监控系统相配合,完成水轮发电机组的开机、停机、增减负荷、紧急停机等任务。当水轮机调速器与其他装置一起工作时,可自动完成功率控制、频率控制、开度控制和水位控制等任务。

大采高综采自动化系统设计与应用

为解决大采高综采工作面大型设备多、采煤工艺复杂等问题,采用大采高综采自动化系统,以提高工作面自动化水平和生产效率,论述了大采高综采自动化系统的控制目标与系统组成,详细介绍地面监控系统、网络传输系统和就地监控系统的组成和作用。从采煤机进刀时间、割煤平整程度、移架行程、移架时间、支架初撑力、刮板输送机推进效果和系统监测等方面,分析了大采高自动化系统在王庄煤矿8101工作面的应用效果,结果表明,进刀时间稳定、割煤平整,移架到位率达92.8%,移架时间相比人工移架提升29.2%,支架初撑力提升11.4%,刮板输送机到位率100%,监测系统效果良好。

基于3种类型驾驶员变安全距离防夹塞变道的车辆控制策略

为了预防他车突然夹塞变道斜插入自车与前车之间间隙的危险驾驶行为,提出了加速跟车的车辆控制策略.在跟车安全距离模型的基础上,考虑车头时距、停车间距等跟车特性,建立基于3种类型驾驶员的跟车安全距离模型和防夹塞安全距离模型.通过Sigmoid函数建立了基于变安全距离控制策略的车速控制器,检测到他车夹塞变道,选择防夹塞安全距离模型;无夹塞车辆时,选择跟车安全距离模型.利用MATLAB/Simulink和CarSim联合仿真平台进行了试验,结果表明基于3种类型驾驶员变安全距离防夹塞变道的车辆控制策略可行.