Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.     

Evolutionary crew scheduling with adaptive chromosomes

This paper presents an adaptive evolutionary approach incorporating a hybrid genetic algorithm (GA) for public transport crew scheduling problems, which are well-known to be NP-hard. To ensure the search efficiency, a suitable chromosome representation has to be determined first. Unlike a canonical GA for crew scheduling where the chromosome length is fixed, the chromosome length in the proposed approach may vary adaptively during the iterative process, and its initial value is elaborately designated as the lower bound of the number of shifts to be used in an unachievable optimal solution. Next, the hybrid GA with such a short chromosome length is employed to find a feasible schedule. During the GA process, the adaptation on chromosome lengths is achieved by genetic operations of crossover and mutation with removal and replenishment strategies aided by a simple greedy algorithm. If a feasible schedule cannot be found when the GA’s termination condition is met, the GA will restart with one more gene added. The above process is repeated until a feasible solution is found. Computational experiments based on 11 real-world crew scheduling problems in China show that, compared to a fuzzy GA known to be well performed for crew scheduling, better solutions are found for all the testing problems. Moreover, the algorithm works fast, has achieved results close to the lower bounds obtained by a standard linear programming solver in terms of the number of shifts, and has much potential for future developments.     

交叉口瞬时交通流量预测的自适应卡尔曼滤波模型

提出了一个交叉口瞬时交通量预测的带时间窗自适应卡尔曼滤波模型（Adaptive Kalman Filter Model,AKFM）;详细阐述了模型的理论基础、建立过程、推导步骤及状态转移矩阵;并通过C＋＋实现了AKFM和传统卡尔曼滤波模型（Traditional Kalman Filter Model,TKFM）;最后,通过实际交叉口的检测数据对该模型与传统卡尔曼滤波模型进行了比较分析及评价.实验结果表明,AKFM模型是稳定有效的,预测精度优于TKFM.     

双电机独立驱动电动车电子差速技术

针对双电机独立驱动电动车电子差速问题进行了研究,根据ACKERMANN汽车转向模型和电机的特性及双电机独立驱动的特点,提出了以2个驱动轮的相对滑转率（6）为控制变量进行调速控制的方法,并确定了6的临界值,在6≤2％时,采用自适应调节的电子差速模式,实现电子差速功能;在占〉2％时,采用闭环有差反馈式调压系统调节,使占≤2％,实现电子差速的自调节功能。仿真模拟结果表明,此电子差速控制策略能够保证电动车在直线和转向行驶达到差速目的,并能以最佳的驱动力行驶。     

车内噪声品质偏好性主客观评价及相关性分析

以5种轿车典型运行工况下后排乘员耳旁噪声样本为评价对象,利用自适应分组成对比较法对车内噪声品质进行了偏好性主观评价试验。计算了各样本的主要心理声学参数,通过相关分析和线性多元回归分析,建立了以响度和尖锐度描述车内噪声品质偏好性结果的数学模型。通过各组间的关联样本将各组评价结果进行反演构建,得到的总体样本评价结果与传统成对比较法评价结果一致性好,且节省了50%的评价时间。     

电液主动悬架的自校正控制

运用衰减记忆递推最小二乘参数估计算法和广义加权最小方差自校正控制算法对主动悬架系统进行控制，并利用压力伺服阀控制的液压缸作为执行器。理论分析和仿真结果表明，所提出的控制系统是可行的，这种自适应自校正算法对系统中一些时变参数具有良好的适应性。     

基于NN-FR的交通中同一检测面上多检测器的数据融合

基于神经网络-模糊推理(N N-FR)的数据融合方法——自适应神经网络-模糊推理信息融合系统(A N N-FRIFS),对交通中同一检测面上的多种检测器采集的数据进行融合。首先简单介绍了A N N-FR IFS,然后分析了AN FIS置信度判别器的设计,并给出了A N N-FRIFS算法,最后结合仿真算例,验证了该方法能以较高精度对同一检测面上的多检测器进行数据融合。     

基于单神经元的汽车方向自适应PID控制

针对汽车方向动力学控制存在的非线性和参数时变不确定性问题，提出了一种新的基于单神经元的汽车方向自适应PID控制算法。该算法利用了神经网络的自学习和自适应能力，实现了方向PID控制器的参数在线自整定，从而避免了传统的自适应PID控制必须在线辨识被控系统的参考模型参数而带来的计算工作量大的问题。仿真计算和场地试验验证表明该控制算法可有效地控制汽车按照预期给定的轨迹行驶，且保证了汽车方向闭环控制系统具有较强的适应性和鲁棒性。     

四轮转向汽车自适应模型跟踪控制研究

使用单点预瞄驾驶员模型，针对确定性汽车模型探讨了４ＷＳ汽车在单移线行驶过程中后轮的最优转向控制规律。通过引入状态反馈，改善了整车的转向特性，将实际汽车的前后轮胎侧刚度及外界干扰视为有界的不确定性参数，采用自适应模型跟踪变结构控制方法，使得不确定的实际汽车模型能够很好地跟踪确定的最优理论模型，仿真结果表明该方法的可行性，控制系统能够有效地克服参数摄动及外界干扰对系统稳定性的影响。