The advancement of communication technologies significantly enhances the capabilities for cooperative control among connected vehicles. In terms of platooning, this paper proposes a delay-dependent cloud-based nonlinear model predictive control algorithm for fuel-saving platoons which requires maintaining a desired longitudinal inter-vehicle gap. Firstly, the platoon is modeled with longitudinal dynamics, considering the constraints of physical limitations and rear-end collision avoidance based on information topology. For random communication delay compensation, a delay-dependent control algorithm is designed and combined with a cloud control strategy to achieve platoon performance and improve fuel economy. This paper conducts a comprehensive analysis of the proposed method's asymptotic stability and string stability, employing Lyapunov techniques for validation. The simulation of a 10-vehicle platoon shows high tracking accuracy and quick consensus convergence as well as high fuel efficiency under the random time delay process and networked control protocol. Finally, the real-vehicle platooning test is conducted, demonstrating that the space errors are maintained within 1 m under high-speed scenarios with realistic communication delays.

Complex traffic scenarios at uncontrolled intersections are crucial for the test validation of autonomous driving systems. The core of the test scenario construction lies in the accurate modeling of the complex interaction behaviors between vehicles in dynamic traffic. Data-driven models are difficult to support long-term simulation due to the existence of cumulative errors. In addition, existing mechanistic models usually assume rational driver behavior and focus mainly on improving efficiency and safety, thus simplifying vehicle interactions. To overcome the limitations of existing studies, we construct a complex traffic interaction model based on social force theory. This model captures the intricate interactions among vehicles at uncontrolled intersections by introducing the concepts of driving and repulsive forces. In particular, we propose a novel concept of segmented conflicting repulsion, an approach that can accurately model high-risk scrambling interactions between vehicles at intersections. Validation of the model using real data sets demonstrates its ability to accurately reproduce complex interaction behaviors at real-world intersections. Further, simulation analysis and application results reveal that our model-generated scenarios significantly outperform those created by SUMO in terms of complexity, thereby effectively enhancing the safety assessment of the autonomous driving system, Apollo.

As autonomous driving technology scales up, complex urban intersections pose significant safety challenges. Current testing methods struggle to simulate these complex scenarios at a manageable cost, making simulation testing essential. For effective evaluation, establishing comprehensive and objective complexity metrics is crucial. However, existing complexity evaluation methods often depend on the performance of the primary vehicle and are based on local interaction relationships, which lack a global perspective and objectivity and have yet to be validated by autonomous driving systems. To address this issue, this paper proposes a multidimensional complexity assessment framework that introduces system-level indicators such as vehicle count, interaction density, disorder, and risk. This framework quantifies the complex interactions at intersections from a global perspective, independent of primary vehicle performance. Experimental results demonstrate that the complexity evaluation results are highly consistent with the performance of a high-level autonomous driving system (Apollo). The framework has been successfully applied to test scenario generation on the Apollo platform, achieving twice the scenario generation efficiency of traditional methods, thus showcasing substantial engineering value.

High-level Automated Vehicles (HAVs) are expected to improve traffic safety significantly. However, verifying and evaluating HAVs remains an open problem. Scenario-based testing is a promising method for HAV testing. Boundary scenarios exist around the performance boundary between critical and non-critical scenarios. Testing HAVs in these boundary scenarios is crucial to investigate why collisions cannot be avoided due to small changes in scenario parameters. This study proposes a methodology to generate diverse boundary scenarios to test HAVs. First, an approach is proposed to obtain at least one High-Performance Classifier (HPC) based on two classification algorithms that iteratively guide each other to find uncertain scenarios to improve their performance. Then, the HPC is exploited to find candidate scenarios highly likely to be boundary scenarios. To increase the efficiency of candidate scenario generation, a strategy based on local sampling is presented to find more diverse candidate scenarios based on a small number of them. Numerical experiments show that the HPCs acquired by the method proposed in this study can achieve a classification accuracy of 98% and 99% for random car-following and cut-in scenarios, respectively. Moreover, more than 86% of 271,744 candidate cut-in scenarios derived by local sampling are near the performance boundary.

为解决小概率高风险边缘测试场景的问题,本文提出一种基于场景动力学和强化学习的边缘场景生成方法,实现边缘场景的自动生成,能模拟真实世界中车辆间的对抗与博弈行为的特征.首先将随时间动态变化的场景模型由一组微分方程描述为场景动力学系统;然后利用神经网络作为通用函数逼近器来构造场景黑盒控制器,并基于强化学习实现边缘场景控制器的优化求解;最后以超车切入场景为例,在Matlab/Simulink软件进行仿真验证,结果表明,边缘场景强化生成模型在场景交互博弈、覆盖率和可重复测试等方面具有良好的性能.

Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.

随着自动驾驶测试验证对虚拟仿真场景依赖程度的增加,传统基于专家经验的场景枚举生成方法已无法满足测试需求.数字虚拟仿真场景自动生成方法在场景多样性、危险性、可解释性、生成效率等方面存在巨大技术优势,是提高汽车自动驾驶技术测试验证安全性和可靠性的关键,已成为当前汽车智能化领域的研究热点.在广泛调研场景自动生成方法领域研究成果的基础上,系统地梳理了场景定义、场景解构、基于机理建模的场景生成、数据驱动的场景生成等方向中最新的研究进展,重点分析了一些值得深入研究的问题,最后对未来可能的研究方向进行展望.场景解构方面,针对场景具有无限丰富、极其复杂、不可穷举特征的问题,应重点关注"场地-气象-交通"耦合的多源异构复杂场景解构方法研究;基于机理建模的场景生成方面,针对多样性、边界性的测试需求,重点关注场景组合生成研究、边界场景优化生成及自适应生成研究等方面;基于数据驱动的场景生成方面,采集内容丰富的数据集是研究的基础,应充分挖掘场景数据的测试价值,重点关注场景重构、加速测试的场景采样、危险场景衍生等方面的研究.未来的研究应重点关注以上几个方面,建立完整的自动驾驶虚拟仿真场景自动生成体系,为L4级及以上的高等级自动驾驶大规模仿真测试评估奠定理论基础.

In order to simulate plausible lane change behavior of traffic vehicles for autonomous driving virtual test, this paper proposes a lane change execution model based on vehicle dynamics. According to linear two-degree-of-freedom dynamics model, a mathematical of the lane-changing trajectory function is derived. To ensure vehicle lane change stability in different driving speed, considering that the maximum lateral acceleration can reflect the aggressiveness of lane change behavior, a trajectory function parameter setting method considering lane change characteristic is proposed. The lane change execution model is verified by the fitting effect on the real lane changing trajectories from NGSIM dataset. The result show that the lane change execution model has the similar expressing ability of the lane-changing trajectory with the geometric curve model, and even better fitting effect. The simulation of the model is implemented in PanoSim, which is an intelligent driving simulation platform. The simulation result show that the discretionary lane changing behavior of traffic vehicles can be realistically simulated by lane change execution model. Moreover, by adjusting the vehicle speed and maximum lateral acceleration can flexibly adjust the aggressiveness of the lane change behavior.

针对场景及其内容定义不明确、场景基本要素提取多为主观分析和不同测试主体的场景要素选择和设计不可解释等问题,本文中提出一种汽车自动驾驶仿真场景的关键要素提取方法.该方法从自动驾驶系统角度逐级分析场景要素对感知、决策和控制模块的影响,并根据对自动驾驶不同子模块的影响,建立为一种要素-结构-功能平面的映射方程,然后基于平面节点判别矩阵建立场景要素的提取模型,量化场景要素的重要性并进行判别和筛选.最后通过分析第四届世界智能驾驶仿真挑战赛的行人安全避撞场景的元素构成,验证了所提方法的可行性和有效性.

随着汽车智能化程度的不断提高,智能汽车通过环境传感器与周边行驶环境的信息交互与互联更为密切,需应对的行驶环境状况也越来越复杂,包括行驶道路、周边交通和气象条件等诸多因素,具有较强的不确定性、难以重复、不可预测和不可穷尽.限于研发周期和成本、工况复杂多样性,特别是安全因素的考虑,传统的开放道路测试试验或基于封闭试验场的测试难以满足智能驾驶系统可靠性与鲁棒性的测试要求.因此,借助数字虚拟技术的仿真测试成为智能驾驶测试验证一种新的手段,仿真场景的构建作为模拟仿真的重要组成部分,是实现智能驾驶测试中大样本、极限边界小概率样本测试验证的关键技术,这对提升智能驾驶系统的压力和加速测评水平显得尤为重要.面向智能驾驶测试的仿真场景构建技术已成为当前汽车智能化新的研究课题和世界性的研究热点,作为一种新兴技术仍面临许多挑战.本文提出了面向智能驾驶测试的仿真场景构建方法,系统阐述了国内外研究工作的进展与现状,包括场景自动构建方法和交通仿真建模方法,重点分析一些值得深入研究的问题并围绕场景构建技术的发展趋势进行了讨论分析,最后介绍了团队相关研究在2020中国智能驾驶挑战赛仿真赛和世界智能驾驶挑战赛的仿真场景应用情况.

目的 研究阿托伐他汀通过调节转化生长因子β(TGF-β)/Smad通路对糖尿病大鼠脑梗死的影响机制.方法 60只雄性SD大鼠,根据随机数字表法选20只为假手术组(正常饲养),另40只随机分为模型组和阿托伐他汀组,给予高脂乳剂喂养2周后,尾静脉注射2％链脲佐菌素,建立糖尿病模型,再选造模成功大鼠用线栓法建立大脑中动脉缺血再灌注模型.假手术组和模型组给予等容积的生理盐水,阿托伐他汀组给予10 mg/kg阿托伐他汀.给药3d后(第4天)用改良神经功能缺损评分(mNSS)评估3组大鼠神经损伤严重程度,造模成功后检测大鼠脑梗死体积、第2和4天糖脂代谢相关指标;用RT-PCR检测脑组织中TGF-β/Smad信号通路相关基因表达.结果 模型组和阿托伐他汀组mNSS评分[(5.46±0.78)分和(3.14±0.45)分vs (0.63±0.13)分]和脑梗死体积[(415.46±80.68)mm3和(243.13±54.43)mm3 vs (8.64±2.12)mm3]均高于假手术组,且模型组高于阿托伐他汀组(P＜0.05).3组糖、脂代谢相关指标差异显著,其中模型组和阿托伐他汀组空腹胰岛素、胰岛素抵抗指数、糖化血红蛋白、TG、TC、LDL-C水平较假手术组显著增加,胰岛素敏感指数和HDL-C较假手术组显著降低,且阿托伐他汀组各项指标变化显著优于模型组,差异有统计学意义(P＜0.05).3组大鼠TGF-β/Smad信号通路相关基因表达水平差异显著,其中模型组、阿托伐他汀组TGF-β、Smad2、Smad4表达水平显著高于假手术组,而p21表达水平显著低于假手术组,且阿托伐他汀组各基因表达水平显著优于模型组,差异有统计学意义(P＜0.05).结论 阿托伐他汀对脑缺血有保护作用,其作用机制可能与调节TGF-β/Smad信号通路有关.

目的 探讨重症脑卒中患者并发危重症性多发性神经病(CIP)的发生率以及其危险因素.方法 148例重症脑卒中患者,在发病后1周内和1个月后各进行1次四肢EMG检查,观察并统计:(1)CIP发生率.(2)并发CIP的患者和未并发CIP的患者一般情况比较.(3)并发CIP可能的危险因素分析.结果 (1)共有23例患者并发CIP(15.54％).(2)并发CIP和未并发CIP的两组患者之间进行比较,在性别比例、基础疾病构成、入院时急性生理学及慢性健康状况评分系统Ⅱ(APACHEⅡ)评分、昏迷、高血糖、静脉使用神经阻滞剂和静脉使用肾上腺糖皮质激素方面均无统计学差异(均P＞0.05),并发CIP患者在年龄、并发脓毒症、并发多器官功能衰竭(MOF)、建立人工气道情况、有创机械通气、并发营养不良和使用肠外营养方面显著高于未并发CIP患者(均P＜0.05).(3)重症脑卒中患者并发CIP的危险因素有60岁以上、脓毒症、MOF、建立人工气道、有创机械通气、营养不良和使用肠外营养;Logistic多因素回归分析发现,脓毒症、MOF、有创机械通气、营养不良是重症脑卒中患者并发CIP的独立危险因素.结论 脓毒症、MOF、有创机械通气、营养不良是重症脑卒中患者并发CIP的独立危险因素.

[目的]探讨高压氧治疗对急性脑梗死患者的躯体功能障碍及日常生活活动能力影响.[方法]将360例符合急性脑梗死患者,随机分为A、B2组,A组(n=180)接受常规抗血小板、稳定斑块、调脂及早期康复等治疗,B组(n=180)在A组的基础上联合高压氧治疗.观察期4周,治疗前与治疗后均应用NIHSS评分、Fugl-Meyer躯体功能量表及Barthel指数评估,统计分析治疗效果.[结果]2组患者治疗前的上述评估指标无明显统计学差异,经过4周治疗后均有明显改善,且B组改善优于A组.[结论]高压氧治疗联合传统药物及早期康复治疗可以更显著地改善脑梗死患者躯体功能障碍和日常生活能力.

[目的]观察低频脉冲电磁疗联合常规药物对慢性紧张型头痛患者的治疗效果.[方法]慢性紧张型头痛患者146例,随机分为A、B2组,A组患者(n=73)接受常规非甾体抗炎药物和肌松药物治疗,B组患者(n=73)在常规药物治疗基础上应用低频脉冲电磁疗治疗,观察4周后头痛缓解程度和6月内平均每月发作的天数,头痛程度采用数字疼痛评分法(VAS评定).[结果]2组患者经过治疗4周后头痛程度均有不同程度缓解,A组患者头痛缓解程度不如B组,6月内A组平均每月发作天数多于B组,差异均有统计学意义.[结论]低频脉冲电磁疗对慢性紧张型头痛患者有明显疗效.

目的 观察早期康复治疗对急性脑卒中患者转化生长因子β1(TGF-β1)和C-反应蛋白(CRP)水平的影响.方法 选取2014年5月至2017年5月开封市中心医院收治的240例急性脑卒中患者,其中急性脑梗死180例,脑出血60例,按照随机数表法分为两组,各120例.A组患者病情稳定24 h后开始进行康复治疗,B组患者病情稳定1周后开始进行康复治疗.观察患者发病24 h内及第7、14、28天时的TGF-β1及CRP水平,并比较两组患者出院时Bathel指数.结果 两组患者各个时期TGF-β1比较,A组患者在第7天和第14天的水平低于B组,差异有统计学意义(P<0.05);两组患者各个时期CRP比较,A组患者在第7天和第14天的水平低于B组,差异有统计学意义(P<0.05).两组患者入院时Bathel指数比较,差异无统计学意义(P>0.05);出院时两组患者Bathel指数均较入院时高,且A组高于B组,差异有统计学意义(P<0.05).结论 早期进行康复治疗能够促进急性脑卒中患者TGF-β1及CRP的炎症反应吸收,提高Bathel指数,改善生活质量.

目的 观察肌电生物反馈对脑卒中偏瘫伴有足下垂、足内翻患者的治疗效果.方法 选取270例脑卒中偏瘫伴有足下垂、足内翻患者,按随机数表法分为A、B两组,各135例.A组接受常规康复治疗,B组在常规康复治疗基础上加用肌电生物反馈治疗,两组均治疗12周.对比两组患者治疗前后平地10 m步行速度和主动关节活动度(AROM).结果 两组患者治疗后平地10 m步行速度均高于治疗前,B组高于A组,差异有统计学意义(P<0.05).两组患者治疗后AROM均大于治疗前,B组大于A组,差异有统计学意义(P<0.05).结论 肌电生物反馈联合常规康复治疗可进一步改善脑卒中偏瘫伴有足下垂、足内翻的运动功能,值得推广应用.

Ischemic stroke is the leading cause of worldwide mortality and long-term disability in adults. This study aims to explore the effects of RNA interference (RNAi)-mediated silencing of the S100B gene on nerve function recovery and morphological changes of hippocampus cells in rat models with ischemic stroke. Sixty Wistar rats were assigned into different group. S100B and Caspase 3 mRNA and protein expressions were evaluated by RT-qPCR and Western blotting. Positive rate of S100B, NeuN, and MAP2 expressions were detected by immunohistochemistry (IHC). Water content, malondialdehyde (MDA) levels, and superoxide dismutase (SOD) activity in brain tissues were measured. Enzyme-linked immunosorbent assay (ELISA) was employed to detect serum levels of TNF-α and IL-1β. A neurological severity score (NSS) was used to test nerve function. TUNEL assay was used to determine hippocampal cell apoptosis. Downregulation of S100B showed a lower number of S100B immune positive cells, but higher NeuN and MAP2-positive cells, increased SOD level, declined MDA level, prominently faster recovery of neurological function, decreased TRCS, TCTP, TCFP, and IE levels, an obvious increase in the number of survival neurons, a decrease in the number of apoptotic cells, notably decreased TNF-α and IL-1β contents, as well as infarct volume, an obvious decrease in positive hippocampal cell Caspase 3 expression and protein expressions of Caspase 3 and cleaved Caspase 3. This study provides data to suggest that RNAi-mediated silencing of S100B gene could improve the recovery of nerve function while inhibiting apoptosis of hippocampal cells in rats with ischemic stroke.

目的 探讨以短暂性脑缺血发作(transient ischemic attack,TIA)为表现的患者ABCD2评分与脑磁共振DWI高信号的关系.方法 326例临床表现为TIA患者,于发病1周内入院,并查头颅DWI了解是否有高信号.按照DWI是否有高信号分为脑梗死组与非脑梗死组.比较2组ABCD2评分及不同危险度分层的患者脑梗死比例.结果 ABCD2评分无统计学意义(P>0.05).结论 ABCD2评分与脑磁共振DWI高信号有关,但对于预测发生脑梗死作用有限.