Research on X-by-Wire Chassis Technology for Intelligent Driving of New Energy Vehicles

As China’s economy develops,new energy technologies and intelligent driving have become a trend in the automobile industry.The development of new energy vehicles has accelerated,with X-by-wire chassis technology becoming the core technology for intelligent driving.This technology includes steer-,brake-,shift-,and throttle-by-wire systems.It is not only the key technology for new energy vehicles but also an important support for promoting their sustainable development.This article presents an in-depth study on X-by-wire chassis technology in new energy vehicles and its basic working principle.

An Intelligent Non-Collocated Control Strategy for Ball-Screw Feed Drives with Dynamic Variations

The ball-screw feed drive has varying high-order dynamic characteristics due to flexibilities of the slender screw spindle and joints between components, and an obvious feature of non-collocated control when a direct position measurement using a linear＇scale is employed. The dynamic characteristics and non- collocated situation have long been the source of difficulties in motion and vibration control, and deterio- rate the achieved accuracy of the axis motion. In this study, a dynamic model using a frequency-based sub- structure approach is established, considering the flexibilities and their variation. The position-dependent variation of the dynamic characteristics is then fully investigated. A corresponding control strategy, which is composed of a modal characteristic modifier （MCM） and an intelligent adaptive tuning algorithm （ATA）, is then developed. The MCM utilizes a combination of peak filters and notch filters, thereby shaping the plant dynamics into a virtual collocated system and avoiding control spillover. An ATA using an artificial neural network （ANN） as a smooth parameter interpolator updates the parameters of the filters in real time in order to cope with the feed drive＇s dynamic variation. Numerical verification of the effectiveness and robustness of the orooosed strategy is shown for a real feed drive.

A Hardware Platform Framework for an Intelligent Vehicle Based on a Driving Brain

The type, model, quantity, and location of sensors installed on the intelligent vehicle test platform are different, resulting in different sensor information processing modules, The driving map used in intelligent vehicle test platform has no uniform standard, which leads to different granularity of driving map information, The sensor information processing module is directly associated with the driving map information and decision-making module, which leads to the interface of intelligent driving system software module has no uniform standard, Based on the software and hardware architecture of intelligent vehicle, the sensor information and driving map information are processed by using the formal language of driving cognition to form a driving situation graph cluster and output to a decision-making module, and the out- put result of the decision-making module is shown as a cognitive arrow cluster, so that the whole process of intelligent driving from perception to decision-making is completed, The formalization of driving cognition reduces the influence of sensor type, model, quantity, and location on the whole software architec- ture, which makes the software architecture portable on different intelligent driving hardware platforms.

Analysis of the Application of Artificial Intelligence in Transportation

With the advancement of the information age,the transportation industry has experienced rapid growth,leading to an expansion in the scale and number of highway constructions.However,this development has also given rise to numerous traffic issues,including frequent vehicle congestion and traffic accidents.To address these problems,it is essential to leverage modern technology for real-time information collection and analysis,providing robust technical support for intelligent transportation systems.This paper focuses on artificial intelligence(AI)technology,explaining its concept and its role in intelligent transportation.It reviews the various application areas and analyzes the use of AI in intelligent transportation.Finally,it proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.

Human-Machine Shared Lateral Control Strategy for Intelligent Vehicles Based on Human Driver Risk Perception Reliability

Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.

Intelligent speed adaptation for visibility technology affects drivers’speed selection along curves with sight limitations

Sight obstructions along road curves can lead to a crash if the driver is not able to stop the vehicle in time.This is a particular issue along curves with limited available sight,where speed management is necessary to avoid unsafe situations(e.g.,driving off the road or invading the other traffic lane).To solve this issue,we proposed a novel intelligent speed adaptation(ISA)system for visibility,called V-ISA(intelligent speed adaptation for visibility).It estimates the real-time safe speed limits based on the prevailing sight conditions.V-ISA comes with three variants with specific feedback modalities(1)visual and(2)auditory information,and(3)direct intervention to assume control over the vehicle speed.Here,we investigated the efficiency of each of the three V-ISA variants on driving speed choice and lateral behavioural response along road curves with limited and unsafe available sight distances,using a driving simulator.We also considered curve road geometry(curve direction:rightward vs.leftward).Sixty active drivers were recruited for the study.While half of them(experimental group)tested the three V-ISA variants(and a V-ISA off condition),the other half always drove with the V-ISA off(validation group).We used a linear mixed-effect model to evaluate the influence of V-ISA on driver behaviour.All V-ISA variants were efficient at reducing speeds at entrance points,with no discernible negative impact on driver lateral behaviour.On rightward curves,the V-ISA intervening variant appeared to be the most effective at adapting to sight limitations.Results of the current study implies that V-ISA might assist drivers to adjust their operating speed as per prevailing sight conditions and,consequently,establishes safer driving conditions.

Cloud Control System Architectures,Technologies and Applications on Intelligent and Connected Vehicles:a Review

The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.

Cooperative Intelligence for Autonomous Driving

Autonomous driving is an emerging technology attracting interests from various sectors in recent years.Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent modules.In this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving techniques.We first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for vehicles.Following this,a cooperative intelligence framework is proposed for autonomous driving systems.This general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

基于平行测试的认知自动驾驶智能架构研究

在大数据、云计算和机器学习等新一代人工智能技术的推动下,自动驾驶的感知智能在近年来得到显著的提升与发展.然而,与人类驾驶过程中隐含的以自我目的实现为引导的自探索性和自主性相比,现阶段自动驾驶技术主要以辅助驾驶功能为主,还停留在以被动感知、规划与控制为主的初级智能自动驾驶阶段.为实现车辆智能从数据驱动的环境感知、辅助决策、被动规划到知识驱动的场景认知、推理决策、主动规划的提升,亟需增强车辆自身对复杂外界信息归纳提炼、推理决策、评价估计等类人能力.首先回顾自动驾驶关键技术演化及其应用发展历程;随后分析测试对车辆智能评估的效用;然后基于平行测试理论,提出自动驾驶车辆认知智能训练、测试与评估空间的构建方法,并设计基于平行测试的认知自动驾驶智能训练框架.该项研究工作预期能为推动自动驾驶从感知智能向认知智能的升级提供可行的技术支撑与实现路径.

针对自动驾驶智能模型的攻击与防御

近年来,以深度学习算法为代表的人工智能技术为人类生产生活的方方面面带来了巨大的革新,尤其是在自动驾驶领域,部署着自动驾驶系统的智能汽车已经走进入们的生活,成为了重要的生产力工具.然而,自动驾驶系统中的人工智能模型面临着潜在的安全隐患和风险,这给人民群众生命财产安全带来了严重威胁.本文通过回顾自动驾驶智能模型攻击和防御的相关研究工作,揭示自动驾驶系统在物理世界下面临的安全风险并归纳总结了相应的防御对策.具体来说,本文首先介绍了包含攻击面、攻击能力和攻击目标的自动驾驶系统安全风险模型.其次,面向自动驾驶系统的三个关键功能层——传感器层、感知层和决策层,本文依据受攻击的智能模型和攻击手段归纳、分析了对应的攻击方法以及防御对策,并探讨了现有方法的局限性.最后,本文讨论和展望了自动驾驶智能模型攻击与防御技术面临的难题与挑战,并指出了未来潜在的研究方向和发展趋势.

工业互联网使用如何促进中小企业智能化转型:驱动因素与赋能机制

工业互联网是中小制造企业智能化转型的重要载体。基于TOE框架与企业能力理论,构建工业互联网使用驱动因素及赋能机制综合研究模型。结果表明:第一,工业互联网使用的重要驱动因素包括资源就绪、数字能力、业务复杂度与政府支持。资源就绪是中小制造企业使用工业互联网的前提,数字能力是中小制造企业使用工业互联网的技术基础,业务复杂度可以反映中小制造企业使用工业互联网的内在需求,政府支持可为中小制造企业使用工业互联网提供保障。第二,工业互联网通过培育两种核心能力赋能企业转型升级。工业互联网使用对中小制造企业智能化转型具有显著赋能效应,集成互联能力通过促进企业互联互通实现智能化转型,协同融合能力通过促进企业内外协同实现智能化转型。结论可为中小企业“上云上平台”和智能化转型提供理论依据与实践启示。

未来教育视域下高校新型混合式教学研究

未来教育是一种畅想和育人方向,既要保持教育的本真,也要紧跟时代的步伐,在技能与想象、工具性与价值性之间保持动态平衡。第四次工业革命诞生下的“互联网+”时代,教育衍生出多种学习和教学新形态。当今世界数字技术蓬勃兴起,生成式人工智能为高校教学带来机遇与挑战,混合式教学异军突起成为教学形式的主力军,也是未来教育教学的“新常态”表现之一。混合式教学一种特殊的社会生产也在走向内涵式发展,以强化教学互动多种实现方式为基石,以沉浸式教学环境为前提,以描摹数字画像促进多元评价为保障,不断探索混合式教学多种实现方式,积极重构教育新常态。

Intelligent Metal Detection and Disposal Automation Equipment Based on Geometric Optimization Driving Algorithm

In order to solve the problem of metal impurities mixed in the production line of wood pulp nonwoven raw materials,intelligent metal detection and disposal automation equipment is designed.Based on the principle of electromagnetic induction,the precise positioning of metal coordinates is realized by initial inspection and multi-directional re-inspection.Based on a geometry optimization driving algorithm,the cutting area is determined by locating the center of the circle that covers the maximum area.This approach aims to minimize the cutting area and maximize the use of materials.Additionally,the method strives to preserve as many fabrics at the edges as possible by employing the farthest edge covering circle algorithm.Based on a speed compensation algorithm,the flexible switching of upper and lower rolls is realized to ensure the maximum production efficiency.Compared with the metal detection device in the existing production line,the designed automation equipment has the advantages of higher detection sensitivity,more accurate metal coordinate positioning,smaller cutting material areas and higher production efficiency,which can make the production process more continuous,automated and intelligent.

技术与商业驱动的群智创新设计

目的结合人工智能时代下社会面临的新问题及现代设计的新使命,提出了技术与商业驱动的群智创新设计思维。方法以设计全球化、新一代人工智能及数字化生存现状为时代背景,阐述了群智创新设计在新时代下的创新本质,并从技术和商业的多元视角阐述了群智创新设计的多元内涵。结果构建了以设计为牵引,技术与商业驱动的群智创新设计模型,并结合具体案例阐述了群智创新设计的优势。结论群智创新设计注重智能技术和群体智慧的结合,是一场复杂系统层面的螺旋式创新演化。以设计为牵引、技术为支撑、商业为激励的群智创新设计方法借用现代网络平台优势及大数据、人工智能、区块链等信息技术赋能,利用群体智慧的协同作用,聚集社会中更多、更好的知识源,以助力创新创造,为社会创造共赢价值。

以学习操作系统构建数字化转型的数智动能

伴随着数据要素价值的凸显,如何以教育数据的有效治理,充分发挥数智驱动教育变革的强大潜力,成为教育数字化转型的关键所在。在智能教育大脑的隐喻下,学习技术系统依托于充当“数智大脑”角色的核心构件,搭建“数据组织—数据建模—数据分析”的教育数据治理通路,深度挖掘并最大化释放教育数据价值。基于此,文章以数据为主线重塑新一代学习技术系统框架,并以学习操作系统作为核心构件的隐喻,从“为何”“是何”及“如何”三个方面深度阐释其来源、内涵与体系架构,聚焦于数智动能的系统实现。同时,基于团队研发的“数智大脑”平台,文章以案例故事的形式描绘其在学校教育中的多元化使用场景,展现出以学习操作系统构建的数智动能的强大应用潜力,以期为数智驱动教育数字化转型提供全新的视角和思路。

中国智能网联车险之现实困境与创新路径

在智慧交通新时代,智能网联车险面临着新的机遇和挑战。本文分析了国内外智能网联车险的发展现状和特征;从智能网联汽车衍生的新兴风险、车险市场经营管理面临的挑战、车险产品和服务的创新需求三方面,探讨了智能网联车险发展的现实困境。基于国外智能网联车险模式的经验借鉴,提出政府、监管部门、保险公司与科技公司合作共赢,进一步完善智能网联车险相关法律体系,创新监管模式,完善车险生态环境,提供优质车险产品与服务等政策建议。

车辆视角下的行人穿行意图识别与行为预测

预测行人穿行马路的行为一直是智能驾驶汽车研究领域的重点方向,对于行人安全保护至关重要。现有研究通常使用轨迹或姿势对行人的行为进行建模,但是行人行为复杂多变,需对其进行更加深层的语义解读。将行人的穿行意图与行为相结合,设计多任务网络来识别行人意图并预测行人行为。行人的穿行意图会影响其行为,故使用行人未来的行为作为先验来检测行人目前的意图和行为,同时考虑到行人周围的交通目标与自车运动对于行人的影响,设计特征融合模块来融合行人特征与交通目标特征。在自动驾驶数据集(PIE和JAAD)上验证实验模型,结果表明,该模型展现了其在行人意图与行为建模方面的优势。

行人过街模拟及车辆右转避障路径规划方法

为解决无信号十字路口右转车辆与同侧过街行人的交互冲突问题,提出一种模拟过街行为的行人过街运动模型,设计了车辆横纵向解耦避障路径规划算法,并进行了仿真实验。使车辆面向动、静态行人时能合理切换避障路径规划策略;同时,将过街运动模型驱动下的行人作为车辆避障对象,以过街模型输出的行人未来轨迹生成车辆纵向速度规划障碍位移—时间区域,从而让行人未来运动状态反馈到车辆避障中。结果表明:本文的行人过街运动模型相对观测值的准确率达到了90%,因此,该模型复现了行人过街过程;能根据行人运动状态切换避障方案,使车辆安全避让过街行人。

养老机构服务智慧化转型驱动因素组态与路径识别——基于模糊集的定性比较分析

智慧化转型是人口老龄化背景下创新养老服务模式、重塑养老服务格局的重要战略选择。养老机构服务对象辐射范围广、服务供给形式灵活、运营管理形式多样,在推动养老服务智慧化转型进程中具有先天优势,养老机构服务智慧化转型可引领、带动实现养老服务体系整体优化升级。基于技术—组织—环境(TOE)框架,运用模糊集定性比较分析(fsQCA)方法探索养老机构服务智慧化转型的驱动因素组合,考察技术、组织、环境维度7个前因变量对养老机构服务智慧化转型意愿的联动效应及作用路径。结果显示,任何单一前因变量均不能单独成为养老机构服务智慧化转型的驱动因素,而是以技术—组织—环境驱动型和技术—组织驱动型两种驱动类型下的4种组态形式协同促进高转型意愿生成,且前因条件越完备,养老机构越倾向于提供智慧化服务;感知相对优势是养老机构服务智慧化转型核心驱动要素,技术兼容性和市场拉力可与特定前因条件“等效替代”共同推动养老机构服务智慧化转型。研究结论为激发养老机构服务智慧化转型动力、探索适宜路径、加速体系智慧化升级提供了极具实践价值的改革参考与管理启示。

有限理性决策理论下驾驶信息过载研究及应用

随着汽车网联化和智能化的发展,汽车驾驶过程面临着车内外多源信息过载的影响,这可能会产生交通安全问题.驾驶信息过载包含感知、认知和决策等复杂神经机制.在知识、时间和精力有限的情况下,当驾驶员处于庞大、繁杂、多变环境中排查信息对象并进行决策时,决策都是在有限度的理性条件下进行,且常带有主观因素,这是有限理性决策理论的基本原理.汽车工业界基于有限理性决策理论,根据人的特性研究了AR-HUD控制驾驶信息过载的汽车驾驶辅助技术以及OLED车载显示的应用,驾驶员在面临信息过载时可快速筛选关键信息,做出正确驾驶决策,以此来提升驾驶的安全性.本文通过综述国内外学者在驾驶信息过载原因、生理心理机制以及控制驾驶信息过载的技术和装备等方面的研究成果,为安全驾驶、汽车智能化发展的产学研结合提供参考.