Intelligent back-looking distance driver model and stability analysis for connected and automated vehicles

The connected and automated vehicles(CAVs)technologies provide more information to drivers in the car-following(CF)process.Unlike the human-driven vehicles(HVs),which only considers information in front,the CAVs circumstance allows them to obtain information in front and behind,enhancing vehicles perception ability.This paper proposes an intelligent back-looking distance driver model(IBDM)considering the desired distance of the following vehicle in homogeneous CAVs environment.Based on intelligent driver model(IDM),the IBDM integrates behind information of vehicles as a control term.The stability condition against a small perturbation is analyzed using linear stability theory in the homogeneous traffic flow.To validate the theoretical analysis,simulations are carried out on a single lane under the open boundary condition,and compared with the IDM not considering the following vehicle and the extended IDM considering the information of vehicle preceding and next preceding.Six scenarios are designed to evaluate the results under different disturbance strength,disturbance location,and initial platoon space distance.The results reveal that the IBDM has an advantage over IDM and the extended IDM in control of CAVs car-following process in maintaining string stability,and the stability improves by increasing the proportion of the new item.

Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using Cooperative Game Theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.

Lattice models of traffic flow considering drivers' delay in response

This paper proposes two lattice traffic models by taking into account the drivers＇ delay in response. The lattice versions of the hydrodynamic model are described by the differential-difference equation and difference-difference equation, respectively. The stability conditions for the two models are obtained by using the linear stability theory. The modified KdV equation near the critical point is derived to describe the traffic jam by using the reductive perturbation method, and the kink-antikink soliton solutions related to the traffic density waves are obtained. The results show that the drivers＇ delay in sensing headway plays an important role in jamming transition.

Different human-simulated characteristics of the driver models in the forward simulation of hybrid electric vehicles

Based on the control theories of PID, fuzzy logic and expert PID, the driver models are built and applied in the forward simulation for hybrid electric vehicles （HEV）. The impact to the vehicle speed tracking and the fuel economy is compared among the different driver models. The different human-simulated characteristics of the driver models are emphatically analyzed. The analysis results indicate that the driver models based on PID, simple fuzzy logic and expert PID are corresponding to the handling characteristics of different drives. The driver models of different human-simulated characteristics bring the handling divergence of drivers with different driving level and habit to the HEV forward simulation, and that is significant to the all-around verification and validation of the control strategy for HEV. System simulation results of different driver models validate the impact of driver models to the dynamic and fuel economy performance of HEV.

An extended smart driver model considering electronic throttle angle changes with memory

Based on the fact that the electronic throttle angle effect performs well in the traditional car following model,this paper attempts to introduce the electronic throttle angle into the smart driver model(SDM)as an acceleration feedback control term,and establish an extended smart driver model considering electronic throttle angle changes with memory(ETSDM).In order to show the practicability of the extended model,the next generation simulation(NGSIM)data was used to calibrate and evaluate the extended model and the smart driver model.The calibration results show that,compared with SDM,the simulation value based on the ETSDM is better fitted with the measured data,that is,the extended model can describe the actual traffic situation more accurately.Then,the linear stability analysis of ETSDM was carried out theoretically,and the stability condition was derived.In addition,numerical simulations were explored to show the influence of the electronic throttle angle changes with memory and the driver sensitivity on the stability of traffic flow.The numerical results show that the feedback control term of electronic throttle angle changes with memory can enhance the stability of traffic flow,which shows the feasibility and superiority of the proposed model to a certain extent.

基于DEMATEL-AISM的起重驾驶员态势感知与行为响应模型

为深入挖掘起重驾驶员态势感知信息-行为的因果关系,结合ENDSLEY态势感知模型与决策实验室分析(DEMATEL)-对抗解释结构模型(AISM)方法,构建起重驾驶员态势感知与行为响应模型。首先,以起重作业任务为研究对象,使用态势感知理论分析驾驶员行为响应过程,获取信息要素;其次,基于DEMATEL法,量化研究要素间的相互关系,得到综合影响矩阵,进而分析要素的属性及特征值,确定模型的关键要素;最后,采用AISM获得原因-结果属性稳定的层级结构,据此构建起重驾驶员态势感知与行为响应信息模型。结果表明:得到包含轨迹预测与规划、避碰等关键要素的22个要素及影响关系构成的5层信息模型;而且,模型明确了信息要素属性特征、影响关系及影响程度,较好地解释了驾驶员态势感知及行为响应过程。

基于驾驶员意图采集的车辆状态控制系统分析

驾驶员操作意图是整车状态变化的基础,对整车运行具有重要影响。以驾驶员操作意图为研究的出发点,对驾驶员执行车辆各种操作时,车辆的状态进行分析,主动包括启动自检工况,档位变化、加速、制动、转向等工况;基于分析结果对各工况的控制流程进行分析和设计;提出了实现车辆基本功能的控制模式,并对控制电路进行设计。在此基础上,应用实车调试,对控制系统进行了测试。结果可知:对车辆进行准确控制的前提是对驾驶员意图进行准确的分析采集,控制流程应简洁明了,尽可能减少控制器的执行周期,缩短控制周期;实车测试表明系统能准确的实现驾驶员的操作意图,实现加速、减速、制动等,表明所设计的控制系统的可靠性与准确性,为此类设计提供参考。

一种低耦合电容的高压SiC MOSFET驱动隔离电源设计

高压SiC MOSFET更快的电压变化率dv/dt,导致其驱动遭受更严重的共模干扰,而现有高隔离电压驱动电源大多又存在耦合电容高、共模瞬态抗扰度(CMTI)能力弱、转换效率低等问题,因此该文设计一种兼具高隔离电压、高转换效率的低耦合电容驱动隔离电源。首先,基于有源钳位反激变换器,提出一种驱动隔离电源耦合电容等效简化解析模型,并通过仿真、实验验证解析模型可行性;其次,基于该模型分析耦合电容影响因素及其优化方法,为低耦合电容的驱动电源设计提供参考;最后,通过实验评估所提低耦合电容高压SiC MOSFET驱动隔离电源性能。结果表明,该文驱动隔离电源额定转换效率约80%,工频耐压高达18 kV,且耦合电容不足2 pF,CMTI能力强。

船舶远程驾驶人机主从博弈控制方法研究

针对船舶远程驾驶场景下的人机目标非一致性问题,本文设计了系统人主机辅的运行模式,提出一种主从博弈框架下的共享控制方法,将船舶协同避碰转向任务中的人机交互作用描述为完全信息条件下的非合作博弈关系,通过构造驾驶员和共驾控制器的状态空间推导主从博弈的Stackelberg微分对策,使用Fan-GFlicksberg不动点定理证明纳什均衡解的存在性与唯一性。根据驾驶风格和操纵技能预分配驾驶权重,基于模型预测控制方法设计轨迹跟踪器,采取反馈校正方式在有限时域内滚动优化,综合考虑船舶安全航行边界、碰撞风险和人机冲突程度在线进行调节。选取船舶横向偏移误差和驾驶员操作负荷等评价指标,在内河操纵环境下验证了方法的有效性。仿真结果表明:本文所提出的主从博弈控制方法能够为具备不同驾驶风格和操纵技能的作业人员提供个性化辅助,当驾驶员和共驾控制器发生意图冲突时,根据航行风险对预分配权重进行调节,在确保航行安全的前提下使船舶尽可能满足驾驶员的操纵意图。

拥堵车源的区域多层交通网络混合路径诱导策略

为了缓解高速公路交通拥堵,利用国道和省道实现交通分流,并有针对性地实施路径诱导策略,建立了考虑拥堵车源的区域多层交通网络混合路径诱导模型。首先,构建了由国道、省道以及湖南省高速公路组成的区域多层交通网络,标定网络链接权重。然后,定位交通拥堵车源,建立了考虑拥堵车源的混合路径诱导模型,对从拥堵车源出发的车辆实施有针对性的路径诱导,在保障缓解交通拥堵效果的同时,尽可能减少对出行者的影响。研究结果表明,该模型能够实现多层网络有效分流,有效减少路网中车辆的平均出行时间。在实际应用中,可以只对少数出行者提供路径诱导信息以降低模型的实施难度,并通过调查出行者对建议路径的接受率,制定合适的路径诱导信息发布方案。

统一驱动与能力抽象结合的无人机载荷虚拟化方法及实现

随着无人机及其集群应用不断深入并日益广泛,无人机载荷呈现种类多样、形态异构、接口复杂等特点,但同时要求系统能快速接入并高效管理载荷。为了解耦无人机载荷与系统平台的紧密关系,扩展对异构载荷的兼容能力,提出一种新型的载荷设备虚拟化方法。提出分层驱动模型,实现载荷硬件设备和驱动软件的解耦,使载荷设备接入过程标准化,该模型通过软硬件协同实现;提出基于“能力”的载荷抽象,解耦载荷应用功能及其设备实体;给出了载荷虚拟化方法具体实现及其典型验证实例。上述载荷设备虚拟化方法通过载荷应用、载荷驱动及载荷设备的软硬件解耦,增强了系统接入不同载荷的兼容性及扩展性,提高了统一管理多种载荷的效率。

应用机器学习算法分析广西林火发生驱动因素及林火预测

森林火灾对生态环境和人类社会构成了严重威胁,当前全球气候不断变化、人类活动日益频繁,森林火灾的影响也日益凸显。以广西壮族自治区为研究区,根据2006—2020年研究区的卫星监测森林火点数据,结合气象数据、地形数据、植被数据和人为活动数据,应用反向传播神经网络(BPNN)、梯度增强决策树(GBDT)、随机森林(RF)、支持向量机(SVM)和极限梯度提升模型(XGBoost)等机器学习算法对广西地区的森林火灾建模,对林火发生概率进行预测;利用最优模型绘制了季节性森林火险区域图,分析森林火灾发生的驱动因素和潜在的森林火灾风险。结果表明:(1)XGBoost模型在预测广西地区森林火灾风险方面表现最佳,其准确率为92.33%,精确度为92.89%,召回率为91.88%,F_(1)值为92.38%,A_(UC)值为97.68%。(2)广西地区森林火灾的主要驱动因素为气象条件与植被因素,主要因素为潜在蒸发量(Pes)、大气压(Sfp)、总初级生产力(GPP)和增强型植被指数(Evi)等。(3)广西地区的春季和冬季是森林火灾的高发季节,中高风险区主要集中在桂东、桂中南和桂西地区。

长白山地区土壤水蚀时空演变特征及驱动因素分析

探讨长白山地区受极端降水、地形复杂、植被破坏等因素影响造成的土壤侵蚀状况,可以研判该地区水土流失情况,为当地水土生态保护提供科学参考。利用修正的通用土壤流失方程(RUSLE)、趋势及相关分析法,定量分析长白山地区土壤水蚀的空间格局及时空分布特征,并探究土壤水蚀与气候、植被覆盖度及地形驱动因子的关系。结果表明:2000-2020年长白山地区土壤水蚀模数均值总体呈“西南高,东北低”的空间格局。相较于其他地类,草地、未利用地、林地更易发生土壤水蚀。近21 a来,土壤水蚀模数平均以0.0255 t/(hm^(2)·a)的速度上升,土壤水蚀模数表现为上升趋势的地区占研究区总面积的68.3%,侵蚀等级总体以微度和轻度为主,且轻度侵蚀比例也呈增加趋势,表明该地区土壤侵蚀状况趋于好转,未来吉林市中北部和延边州部分地区的水土流失风险较高。土壤水蚀与降水、海拔高度及坡度具有较为显著的正相关关系,而与气温、植被覆盖度的相关性以负相关为主。海拔大于1000 m或坡度大于20°的草地、林地区域是水土防治的重点地形区。

基于LQR的智能驾驶汽车横纵向控制研究

为了提高智能驾驶汽车跟踪控制器的稳定性和跟踪精度,提出了一种基于线性二次型调节器(LQR)控制算法和驾驶员预瞄模型的横向跟踪控制策略,结合纵向比例-积分-微分(PID)控制算法实现横纵向控制。首先建立带有前馈的LQR控制器,采用梯度下降优化算法优化LQR控制器权重参数,并在此基础上引入驾驶员预瞄模型,设计了基于经验的预瞄距离自适应控制器;其次建立双PID纵向控制器进行速度控制。最后通过Carsim和Matlab/Simulink联合仿真以及实车测试验证,结果表明:仿真工况下最大横向偏差小于0.035 m,最大航向偏差小于0.09 rad,实车测试工况下也能够良好遵循规划轨迹的整体趋势,速度跟踪效果良好且前轮转角与横摆角速度变化平稳。因此,该控制器能够保证较高精度且平稳的轨迹跟踪,在高速状态下更为明显。

驾驶员对高速公路收费政策调整满意度的影响因素研究

为了研究高速公路收费费率调整和驾驶员满意度之间的关系,选取了8个相关因素,以重庆市为例,通过调查问卷、实地勘察、数据分析等方式获取调查数据,并选择基于有序Logit研究模型,从个人方面、车辆因素、出行因素、政策方面等四个维度考虑,最后进行实证结果分析。研究得出:驾驶员的年龄、收入、出行距离、出行时间、出行目的、是否办理ETC、是否了解调价政策和驾驶员满意度之间存在显著相关关系;其中,驾驶员的年龄和驾驶员满意度之间存在负相关关系;驾驶员的收入、出行距离、是否了解调价政策和驾驶员满意度之间存在正相关关系。

基于Driver Studio的PCI设备WDM驱动程序的设计

为了实现PCI数据采集卡的数据传输,必须开发PCI设备的驱动程序。介绍了以DriverStudio中的Driver Works为工具,开发PCI设备WDM驱动程序的方法。探讨了PCI设备的I/O端口访问、中断处理、驱动程序与应用程序之间的通信等主要问题,重点对编写驱动程序的过程和主要例程的代码进行了说明,并实现了驱动程序的安装与调试。最后对实际中遇到的问题及解决方法做了总结,实践证明该驱动程序调试通过,运行稳定。

新疆农业源非二氧化碳温室气体排放变化趋势预测

基于2000-2020年新疆农作物产量、化肥投入、稻田播种面积等统计数据,根据IPCC排放因子法,核算新疆农业源非CO_(2)温室气体(GHG)排放总量,通过对数平均迪氏指数(LMDI)模型对农业源非CO_(2)GHG排放驱动因素进行分解,使用蒙特卡洛模拟结合情景分析法预测其排放趋势。结果表明,研究期内新疆农业源非CO_(2)GHG排放呈现波动上升趋势,增幅达到34.43%,畜牧养殖是主要排放源。驱动因素研究结果表明,2000-2020年,农业经济发展水平和城镇化水平的提高促进了农业源非CO_(2)GHG的排放,贡献排放量分别达4211.74×10^(4)tCO_(2)eq和1016.08×10^(4)tCO_(2)eq。农业非CO_(2)GHG排放强度、乡村人口以及农业结构的降低抑制了农业源非CO_(2) GHG的排放,2000-2020年减排量分别为4163.36×10^(4)tCO_(2)eq、224.84×10^(4)tCO_(2)eq和130.64×10^(4)tCO_(2)eq。农业源非CO_(2)GHG排放在基准和规划情景下均呈上升趋势,但规划情景下的增速快于基准情景,在低碳情景下通过提高农业非CO_(2)GHG排放强度、改善生产结构有效减缓增速,可能在2035年实现负增长。实现农业源非CO_(2)GHG排放达峰需要强化对减排政策的落实,强制约束农业源非CO_(2)GHG排放;降低农业非CO_(2)GHG排放强度,控制因农业生产技术落后所导致的农业源非CO_(2)GHG排放;通过完善减排惩戒激励机制,鼓励技术突破引导新疆农业的低碳转型。

基于自然驾驶数据的驾驶换道风格分析与辨识

为了研究在换道工况下个体驾驶行为及其辨识,基于NGSIM轨迹数据提取532组换道有效数据。分析处理驾驶员换道特征参数,运用K-means算法对驾驶员进行聚类分析,可分为谨慎型、一般型、激进型三类。并对聚类的参数进行统计分析和时频分析验证各类驾驶人的差异性,并提取出有效参数作为辨识模型的输入,基于惯性权重非线性变化和粒子最大速度非线性递减的策略来改进粒子群,建立起基于改进粒子群优化SVM的驾驶人风格辨识模型,对SVM模型的两个关键参数C和g进行优化更新,驾驶人识别准确率可达到96.25%,且与BP神经网络、PSO-SVM、随机森林、KNN算法相比具有更高的识别准确率。

基于驾驶风格和驾驶员不满度的换道决策模型

针对现有的驾驶员不满度模型未考虑驾驶风格而导致换道决策难以适应不同类型驾驶员的问题,改进基于前车车速的驾驶员不满度模型,并结合基于椭圆车辆模型的最小安全距离模型,建立基于驾驶风格和驾驶员不满度的换道决策模型.选取15个问题作为驾驶员行为问卷(driver behavior questionnaire, DBQ)调查内容,利用主成分分析和K-means聚类将驾驶风格分为激进型、谨慎型和普通型3类.设计驾驶模拟器试验,采集不同驾驶风格的驾驶员换道数据,得到不同驾驶风格的驾驶员不满度阈值.通过MATLAB/Simulink和Carsim仿真平台验证换道决策模型的有效性.结果表明:换道决策模型可根据驾驶员不满度正确产生换道意图,保证换道过程的安全性,同时还可以适应不同驾驶风格的驾驶员,使基于驾驶员不满度的换道决策模型更符合驾驶员的实际驾驶习惯.