车辆安全跟驰模式预测的形式化建模方法

由于传统车辆跟驰建模预测方法无法遍历车辆所有可能的系统输入与运行状态的不确定性, 因而不足以从理论上保证对周边车辆安全跟驰行为预测的完整性与可信性. 为此提出车辆安全跟驰模式预测的形式化建模方法. 该方法利用随机可达集的遍历表现特征实现对周边车辆行为预测的不确定性表述, 并通过马尔科夫链逼近可达集的方式表达系统行为状态变化的随机性, 从而完成对周边车辆跟驰行为状态变化的精确概率预估. 为了表达跟驰情形中车辆之间的行为关联影响以及提高在线计算效率, 离线构建了关联车辆在状态及控制输入之间的安全关联矩阵, 描述周边车辆的安全跟驰控制输入选择规律, 并综合相关车辆的当前状态信息, 达到对周边车辆安全跟驰行为的在线分析与预估. 数值验证不仅表明提出的建模方法完备地表述了周边车辆所有的安全跟驰行为及过程, 显著提高了预测的精确度, 也论证了该方法对车辆跟驰控制策略建模分析与安全验证的有效性.     

具有输入未建模动态的纯反馈非线性系统自适应控制

对一类具有状态和输入未建模动态且控制增益符号未知的纯反馈非线性系统,利用非线性变换、改进的动态面控制方法以及Nussbaum函数性质,提出两种自适应动态面控制方案.利用正则化信号来约束输入未建模动态,从而有效地抑制其产生的扰动.通过引入动态信号,有效地处理了由状态未建模动态引起的动态不确定性.通过在总的李雅普诺夫函数中引入非负正则化信号,并利用稳定性分析中引入的紧集,证明了闭环控制系统是半全局一致终结有界的.数值仿真验证了所提方案的有效性.     

非完整轮式机器人的鲁棒同步编队跟踪及镇定控制

本文研究了受到建模不确定性影响和输入限制的非完整轮式机器人的同步编队跟踪和编队镇定问题.首先,基于领航–跟随策略,确定了编队构型的数学表达形式.其次,通过定义含有辅助控制量的跟踪误差,设计了一种具有统一结构的分布式运动学控制器,可使跟随者实现对复杂期望轨迹的跟踪,包括时变轨迹和固定点等.然后,针对建模不确定性影响和输入限制,基于反步法、模糊控制方法和Lyapunov控制理论,设计了一种饱和动力学控制器,使得系统的闭环跟踪误差全局收敛至零点附近有界领域内.最后,通过对比仿真实验,验证了本文控制方法的有效性.     

虚拟驾驶员决策行为模型研究

驾驶决策行为是驾驶行为研究的重要内容.为提高驾驶决策行为建模与仿真的可信度,提出了基于分层的驾驶员决策行为模型.将驾驶决策分为策略层、方法层、行动层和车辆控制层四个层次.重点对驾驶员行动层的决策行为进行实现.使用两点法和PID方法相结合,计算车辆转向角度,使用反应点跟车模型计算车辆纵向行驶速度.通过驾驶员跟车行为仿真,分析了跟车行为模型的稳定性和逼真性,说明了驾驶员决策行为模型的可信性,使驾驶行为模型的输出更加真实.     

有界扰动下异质车辆队列分布式鲁棒经济预测控制

针对有界扰动下异质车辆队列节能与稳定分布式协同控制问题,提出一种新的分布式鲁棒经济模型预测控制(economic model predictive control, EMPC)策略.首先采用不确定误差模型描述有界扰动下异质车辆队列纵向行驶动态特性,再应用tube思想对系统约束进行紧缩设计,补偿有界扰动对系统造成的不确定性.其次,采用局部车辆行驶能耗模型描述车辆队列分布式经济性能优化的有限时域最优控制问题,并利用传统跟踪性能指标设计附加稳定收缩约束函数.进一步,基于系统收缩原理,建立车辆队列闭环系统关于有界扰动的输入-状态稳定性条件.最后,通过与车辆队列传统分布式鲁棒模型预测控制策略的数值仿真对比结果验证了所提出策略的有效性和优越性.     

支持协商的网构软件体系结构行为建模与验证

针对网构软件行为中的不确定性和不完整性,提出了一种支持协商的网构软件体系结构行为建模与验证方法.在建模中,该方法借鉴了UML时序图元素表示法,并增加了建模元素支持行为的不确定与不完整建模.在验证中,除了集成广泛应用的模型检查工具Spin以提供行为模型的验证能力以外,还引入了基于反例引导的抽象-精化过程思想的协商检查,以解决不确定和不完整建模所带来的正确性验证问题.     

T-CPS下考虑人驾车行为影响的混行车辆协同控制

由于传统人驾车(traditional human-driven vehicles,HVs)驾驶行为会受到驾驶员的心理和生理活动的不确定性影响,可能使得车辆频繁地加减速,进而导致混合交通条件下网联自动车(connected and automated vehicles,CAVs)很难快速跟踪此行为.针对这一问题,首先提出一种提前预测传统人驾车行为的组合神经网络.在此基础上,考虑通信时延和车辆运动学特性,设计一种基于交通信息物理系统(transportation-cyber physical system,T-CPS)的混行车群内车辆协同控制策略,使其能够快速跟踪上传统人驾车行为,并对混行车群内网联自动车之间的串稳定性进行分析.最后,在混合交通条件下设置由1辆传统人驾车、1辆领头网联自动车和4辆跟随网联自动车形成的混行车群,利用下一代交通仿真(next generation simulation,NGSIM)车辆轨迹数据选出高质量传统人驾车状态,并通过仿真实验验证所提协同控制策略的有效性和可行性.由仿真实验结果可知,所提协同控制策略可以保证所有的网联自动车能够快速跟踪上传统人驾车行为,为解决新型混合交通带来的新问题提供一定的理论指导和借鉴.     

不确定环境下hCPS系统的形式化建模与动态验证

随着科技的进步,新型复杂系统例如人机物融合系统（Human Cyber-Physical Systems,HCPS）已经与人类社会生活越来越密不可分.软件系统所处的信息空间与人们日常生活所处的物理空间日渐融合.物理空间内环境的复杂多变、时空数据的爆发增长以及难以预料的人类行为等不确定因素威胁着系统安全.由于系统安全需求的增长,系统的规模和复杂度随之增加所带来的一系列问题亟待解决.因此,在不确定性环境下,构造智能、安全的人机物融合系统已经成为软件行业不可回避的挑战.环境不确定性使得人机物融合系统软件无法准确感知其所处的运行环境.感知的不确定性将导致系统的误判,从而影响系统的安全性.环境不确定性使得系统设计人员无法为人机物融合系统软件的运行环境提供准确的形式化规约.而对于安全要求较高的系统,准确的形式化规约是保证系统安全的首要条件.为了应对规约的不确定性,本文提出时空数据驱动与模型驱动相结合的建模方式,即通过使用机器学习算法,基于环境中时空数据对环境进行建模.根据安全软件的典型特征,采用动态验证的方式保证系统的安全,从而构建统一安全的理论框架.为了展示方案的可行性,本文以自动驾驶车辆与人驾驶的摩托车的交互场景为例说明了在不确定性环境下的人机物融合系统的建模与验证的具体应用.     

不确定非线性系统的神经网络智能重构控制

针对一类不确定非线性动态系统,提出了一种基于神经网络动态补偿的鲁棒模型跟随重构控制策略.该方法吸取了线性模型跟随方法的基本思想,通过引入神经网络在线补偿控制器,以克服系统由故障引起的未建模非线性动态的影响,从而提高模型跟随重构控制的动态性能和稳态精度;并且当系统存在模型不确定性时,其输出仍能精确地跟踪理想模型的输出.文中还给出了特定假设条件下闭环重构控制系统稳定性的严格证明.理论分析和仿真研究表明,所提的方法是有效的并可保证闭环系统具有良好的重构性能.     

卫星控制系统离散积分滑模容错控制

卫星控制系统是采样控制系统,对它来讲,不确定性离散系统容错控制方法非常重要.本文提出了一种离散积分滑模容错控制方案,用于调节动量轮有故障的卫星姿态,其主要部分是设计控制器和控制分配方法.本文对含有匹配和不匹配的不确定性的离散多输入多输出系统,设计了一种离散积分滑模控制器,分析了匹配的和不匹配的不确定性对闭环系统稳定性的影响,得出了系统状态的界.应用可达集方法确定控制分配方案时,本文改变可达集表面的搜索顺序,提高了求解速度.以五动量轮卫星模型为例,将本文方法应用于含多故障、健康因子不准确、匹配不确定性和不匹配不确定性的卫星控制系统中,理论和仿真的结果一致.     

Optimal Information Location for Adaptive Routing

One strategy for addressing uncertain roadway conditions and travel times is to provide real-time travel information to drivers through variable message signs, highway advisory radio, or other means. However, providing such information is often costly, and decisions must be made about the most useful places to inform drivers about local conditions. This paper addresses this question, building on adaptive routing algorithms describing optimal traveler behavior in stochastic networks with en route information. Three specific problem contexts are formulated: routing of a single vehicle, assignment of multiple vehicles in an uncongested network, and adaptive equilibrium with congestion. A network contraction procedure is described which makes an enumerative algorithm computationally feasible for small-to-medium sized roadway networks, along with heuristics which can be applied for large-scale networks. These algorithms are demonstrated on three networks of varying size.     

自动驾驶中量子认知与决策问题研究

自动驾驶车辆对人类驾驶车辆和行人的意图估计及其相互作用研究是极其重要的,现有的研究不能很好的解释人类交通参与者的不确定因素和非理性行为,这对研究自动驾驶车辆在真实道路交通场景中运行形成了阻碍,本文基于量子理论和锚定效应,针对自动驾驶车辆右转时与非机动车和行人交互场景,构建量子决策模型.仿真分析和数据集实验证明了在与人类交通参与者进行交互时,锚定效应下的量子决策模型可以考虑存在不确定性因素和非理性行为时进行加速或减速的决策,且相比于累积前景理论模型(CPT)更加贴合实际情况.     

A digital-driving system for smart vehicles

In the wake of the computer and information technology revolutions, vehicles are undergoing dramatic changes in their capabilities and how they interact with drivers. Although some vehicles can decide to either generate warnings for the human driver or control the vehicle autonomously, they must usually make these decisions in real time with only incomplete information. So, human drivers must still maintain control over the vehicle. I sketch a digital driving behavior model. By simulating and analyzing driver behavior during different maneuvers such as lane changing, lane following, and traffic avoidance, researchers participating in the Beijing Institute of Technology's digital-driving project will be able to examine the possible correlations or causal relations between the smart vehicle, IVISs, the intelligent road-traffic-information network, and the driver. We aim to successfully demonstrate that a digital-driving system can provide a direction for developing human-centered smart vehicles.     

基于模型预测控制的电动车辆队列控制研究

针对车辆队列建模时参数不确定导致控制存在误差的问题,以及队列中跟随车辆稳定性问题,分析车辆纵向动力学,设计一个鲁棒MPC控制器和滑移率控制器来提高队列车辆的控制精度和稳定性.首先对纵向MPC控制器进行改进,提高车辆队列控制精度；同时为防止跟随车辆的轮胎打滑,设计一个MPC滑移率控制器对跟随车辆的轮胎滑移率进行控制约束,保证了跟随车辆的纵向稳定性.最后,进行仿真实验验证其有效性.仿真实验结果表明,与传统的LQR、MPC控制器相比,改进的鲁棒MPC纵向控制器控制精度更高,同时MPC滑移率控制器可防止跟随车辆的轮胎打滑,保证了跟随车辆的纵向稳定性.     

A Framework for Worst-Case and Stochastic Safety Verification Using Barrier Certificates

This paper presents a methodology for safety verification of continuous and hybrid systems in the worst-case and stochastic settings. In the worst-case setting, a function of state termed barrier certificate is used to certify that all trajectories of the system starting from a given initial set do not enter an unsafe region. No explicit computation of reachable sets is required in the construction of barrier certificates, which makes it possible to handle nonlinearity, uncertainty, and constraints directly within this framework. In the stochastic setting, our method computes an upper bound on the probability that a trajectory of the system reaches the unsafe set, a bound whose validity is proven by the existence of a barrier certificate. For polynomial systems, barrier certificates can be constructed using convex optimization, and hence the method is computationally tractable. Some examples are provided to illustrate the use of the method.     

Edge-centric trust management in vehicular networks

A major objective of vehicular networking is to improve road safety and reduce traffic congestion. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network may affect the overall network performance and safety. These vehicles may behave intelligently to avoid detection. To effectively control and monitor such security threats, an efficient Trust Management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers which is the major focus of this work. We propose a hybrid solution, which integrates Edge Computing and Multi-agent modeling in a Trust Management system for vehicular networks. The proposed solution also aims to overcome the limitations of the two commonly utilized approaches in this context: cloud computing and Peer-to-Peer (P2P) networking. Our framework has a set of features that make it an efficient platform to address the major security challenges in vehicular networks including latency, scalability, uncertainty, data accessibility, and malicious behavior detection. Performance of the approach is evaluated by simulating a realistic environment. Experimental results show that the proposed approach outperforms similar approaches from literature for various performance indicators.     

Safe Autonomy Under Perception Uncertainty Using Chance-Constrained Temporal Logic

Autonomous vehicles have found wide-ranging adoption in aerospace, terrestrial as well as marine use. These systems often operate in uncertain environments and in the presence of noisy sensors, and use machine learning and statistical sensor fusion algorithms to form an internal model of the world that is inherently probabilistic. Autonomous vehicles need to operate using this uncertain world-model, and hence, their correctness cannot be deterministically specified. Even once probabilistic correctness is specified, proving that an autonomous vehicle will operate correctly is a challenging problem. In this paper, we address these challenges by proposing a correct-by-synthesis approach to autonomous vehicle control. We propose a probabilistic extension of temporal logic, named Chance Constrained Temporal Logic (C2TL), that can be used to specify correctness requirements in presence of uncertainty. C2TL extends temporal logic by including chance constraints as predicates in the formula which allows modeling of perception uncertainty while retaining its ease of reasoning. We present a novel automated synthesis technique that compiles C2TL specification into mixed integer constraints, and uses second-order (quadratic) cone programming to synthesize optimal control of autonomous vehicles subject to the C2TL specification. We also present a risk distribution approach that enables synthesis of plans with lower cost without increasing the overall risk. We demonstrate the effectiveness of the proposed approach on a diverse set of illustrative examples.     

基于横纵向联合控制的多目标优化车辆跟驰研究

为解决车辆在拥堵环境中因车速波动较大所带来的跟驰平稳性较差、跟踪无效或不安全等问题,提出了基于车辆模型和深度强化学习的多目标优化跟驰方案。首先基于车辆横纵向动力学建立车辆跟驰模型,然后根据车间距误差、速度误差、横向偏差及相对偏航角等,利用深度确定性策略梯度算法得到跟驰车的加速度和转向角,以更平稳安全地控制跟驰车辆。经NGSIM公开驾驶数据集进行测试与验证,该方案可有效地提升跟驰车辆的稳定、舒适与安全性,对保证交通安全和提升道路通行能力具有重要意义。