具有情景感知的智能汽车：从模型到原型的发展毕业论文外文翻译

1、Context-aware smart car: from model to prototypeAbstract: Smart cars are promising application domain for ubiquitous computing. Context-awareness is the key feature of a smart car for safer and easier driving. Despite many industrial innovations and academic progresses have been made, we find a lack

2、 of fully context-aware smart cars. This study presents a general architecture of smart cars from the viewpoint of context- awareness. A hierarchical context model is proposed for description of the complex driving environment. A smart car prototype including software platform and hardware infrastru

3、ctures is built to provide the running environment for the context model and applications. Two performance metrics were evaluated: accuracy of the context situation recognition and efficiency of the smart car. The whole response time of context situation recognition is nearly 1.4 s for one person, w

4、hich is acceptable for non-time critical applications in a smart car.Key words: Smart car, Intelligent vehicle, Context-aware, Ubiquitous computing.doi:10.1631 /jzus.A0820154 Document code: A CLC number: TP39INTRODUCTION Cars are becoming important private places frequently used in daily life. Howev

5、er, they also bring many problems, such as traffic congestions and accidents. A smart car aims at assisting its driver with easier driving, less workload and less chance of getting injured (Moite, 1992). For this purpose, a smart car must be able to sense, analyze, predict and react to the road envi

6、ronment, which is the key feature of smart cars: context-awareness. Lots of technologies have been developed in the past decade, such as intelligent transportation systems(ITS) (Wang et al., 2006) and the advanced driver assistant system (ADAS) (Kkay and Bergholz,2004). However, current smart cars a

7、re not really context-aware. Only a few types of the information of road environments, which is called contexts, are utilized. Besides, most of current smart cars lack complex reasoning. These drawbacks limit the smart cars ability of assisting the driving task efficiently and safely. This research

8、focuses on how to build a context-aware smart car. The remainder of this paper is organized as follows. Section 2 introduces the related work on smart cars. A general description of a smart car is given in Section 3. Section 4 proposes a hierarchical context model for comprehensive definition and cl

9、assification of information in a smart car environment. The smart car prototype, including the hardware infrastructure and software platform, is presented in Section 5. The performance evaluation is shown in Section 6 and the conclusions are given in Section 7.RELATED WORK In the past decade, many r

10、esearches from academic and industrial communities have been made on smart cars. The following is a summary of the major progresses in this field. (1) New manufacturing technology. MIT Media Lab presents a conceptual car, City Car (MIT, 2004), a lightweight electric vehicle. This car employs fully i

11、ntegrated in-wheel electric motors and suspension systems, which are self-contained, digitally controlled, and reconfigurable. With the wireless connectivity and a Google-like information grid, drivers could use the information to navigate in a very intelligent way. (2) Driver assistant system. Auto

12、motive manu-factures implement many novel ideas in their newest series of concept cars. BMWs ConnectedDrive includes BMW Assist, BMW Online and driver assistance systems, supporting lane change warning and parking assistant (Hoch et al., 2007). Mercedes-Benz is developing an intelligent driver assis

13、tance system that utilizes stereo cameras and radar sensors to monitor the surroundings around the car (Benz, 2007).Volvos CoDriver is an intelligent assistant that co-ordinates information, studies the traffic situation and assists the driver (Volvo, 2007). Lexus provides advanced active safety tec

14、hnologies on its LS-series,including an advanced pre-collision system, dynamic driving, electronic brake assistance, and park-assistance systems (Lexus, 2007). (3) Collision avoidance system. The SAVE-IT project develops a central component that monitors the roadway, the states of the vehicle and th

15、e driver, with evaluation of the potential safety benefits (Lee etal., 2004). The Cybercars project addresses navigation, obstacle avoidance and platooning (Parent and Fortelle, 2005). The SAFESPOT project aims at expanding the time horizon for acquiring safety relevant information and improving pre

16、cision, reliability and quality of driving (Giulio, 2007). The prevent project develops preventive safety technologies and in-vehicle systems, which sense the potential danger and take the drivers state into account (Matthias,2006). (4) Driver-vehicle interface. The Adaptive Integrated Driver-vehicl

17、e Interface (AIDE) project tries to maximize the efficiency and safety of advanced driver assistance systems, while minimizing the workload and distraction imposed by in-vehicle information systems (Kutila et al., 2007). The Communication Multimedia Unit Inside Car (COMUNICAR) project aims at design

18、ing aneasy-to-use on-vehicle multimedia human-machine interface. An information manager collects the feed back information and estimates the drivers workload according to the current driving and environment situation (Bellotti et al., 2005). (5) Driver behavior recognition. The driver plays an impor

19、tant role in a smart car. Machine learning and dynamical graphical models, such as HMM (Oliver and Pentland, 2000), Gaussian Mixture Modeling (GMM) (Miyajima et al., 2007) and the Bayesian network (Kumagai and Akamatsu, 2006), can be applied for modeling and recognizing driver behaviors. (6) Communi

20、cation and cooperation. The Car-TALK project enables information transmitting among cars in the vicinity (Reichardt et al., 2002).The COM2REACT Project (2006) establishes a cooperative and multi-level transport virtual sub-center by vehicle-to-vehicle communication and vehicle-to-centre communicatio

21、n. The COOPERS Project (2006) provides local situation information, traffic and infrastructure status information via a dedicated infra-structure to support vehicle communication link. The COVER Project (2006) develops semantic-driven cooperative systems with the main focus on communication between

22、the infrastructure and vehicles. The I-WAY Project (Rusconi et al., 2007) designs an intelligent cooperative system, which provides real-time information from other vehicles in the vicinity and roadside equipments to improve drivers responses.The WATCH-OVER Project (2006) develops a cooperative syst

23、em for the prevention of road accidents involving vulnerable road users, such as motorcycles,bicycles, and pedestrians. The Cooperative Vehicle-Infrastructure Systems (CVIS) Project (2006) creates a unified technical solution allowing all vehicles and infrastructure elements to communicate with each

24、 other in a continuous and transparent way. 7) Safety in vehicles. The Secure Vehicular Communication (SEVECOM) Project provides a full definition and implementation of security requirements for vehicular and inter-vehicular communications (Panos et al., 2006). Vehicular Ad-hoc Network (VANET) secur

25、ity also partly addresses the safety in vehicles (Magda et al., 2002; Hubaux et al., 2004; Raya and Hubaux, 2005; Bryan and Adrian, 2005),which gives the problem statement and proposes the outline of a general solution for VANET. However, we found that most of the work listed above is not fully cont

26、ext-aware. Current work usually focuses on special practical technologies, such as communication, sensing and driver assistance. In addition, the reasoning of contexts for further analysis is not put enough emphasis on. These will limit smart cars to be cars with certain accessories, so different re

27、quirements will result in different smart cars. There is a lack of a common consensus and comprehensive understanding of smart cars in a holistic view. This study attempts to build a smart car from the viewpoint of context-awareness in a bottom-up manner. We want to build a general theoretical found

28、ation and an infrastructure framework for a smart car. All the contexts that can characterize an entity of the driving environment will be collected and defined. Reasoning will play an important role in complex situation analysis. In such a smart car, we can develop different services and applicatio

29、ns without much modification to the current architecture.GENERAL ARCHITECTURE A smart car is a comprehensive integration of many different sensors, control modules, actuators,and so on (Wang, 2006). A smart car can monitor the driving environment, assess the possible risks, and take appropriate acti

30、ons to avoid or reduce the risk. A general architecture of a smart car is shown in Fig.1. (1)Traffic monitoring. A variety of scanning technologies can be used to recognize the distance between the car and other road users. Active environments sensing in- and out-car will be a general capability in

31、the near future (Tang et al., 2006). Lidar-radar- or vision-based approaches can be used to provide the positioning information. The radar and lidar sensors provide information about the relative position and relative velocity of an object. Multiple cameras are able to eliminate blind spots, recogni

32、ze obstacles, and record the surroundings. Besides the sensing technology described above, the car can get traffic information from the Internet or nearby cars. (2)Driver monitoring. Drivers represent the highest safety risk. Almost 95% of the accidents are due to human factors and in almost three-q

33、uarters of the cases human behaviour is solely to blame (Rau,1998). Smart cars present promising potentials to assist drivers in improving their situational awareness and reducing errors. With cameras monitoring the drivers gaze and activity, smart cars attempt to keep the drivers attention on the r

34、oad ahead. Physiological sensors can detect whether the driver is in good condition. (3)Car monitoring. The dynamics of a car can be read from the engine, the throttle and the brake. These data will be transferred by controller area networks (CAN) to analyze whether the car functions normally. (4)As

35、sessment module. It determines the risk of the driving task according to the situation of the traffic, driver and car. Different levels of risks will lead to different responses, including notifying the driver through the Human Machine Interface (HMI) and taking emergency actions by car actuators. (

36、5) HMI. It warns the driver of the potential risks in non-emergent situations. For example, a tired driver would be awakened by an acoustic alarm or vibrating seat. Visual indications should be applied in a cautious way, since a complex graph or a long text sentence will seriously impair the drivers

37、 attention and possibly cause harm. (6)Actuators. The actuators will execute specified control on the car without the drivers commands. The smart car will adopt active measures such as stopping the car in case that the driver is unable to act properly, or applying passive protection to reduce possib

38、le harm in abrupt accidents, for example, popping up airbags.HIERARCHICAL CONTEXT MODEL Contexts are information collected when monitoring the roadway, the car and the driver. To implement a context-aware smart car, we must begin with the context analysis. We develop a hierarchical context model, wh

39、ich is the basis of representation and analysis of the smart car environment.Hierarchical context model We categorize context data into three layers according to the degree of abstraction and semantics:the sensor layer, the context atom layer and the context situation layer, as shown in Fig.2. The s

40、ensor layer is the source of context data, the context atom layer serves as an abstraction between the physical world and semantic world, and the context situation layer provides description of complex facts with fusion of context atoms.Context atoms: ontology definition Each sensor corresponds to a

41、 type of context atom. For each type of context atom, a descriptive name must be assigned for applications to use the contexts. We use ontology to define the name to guarantee the semantic understanding and sharing in smart cars. We use three ontologies as shown in Fig.3(1) Ontology for environment

42、contexts. The environmental contexts are related to physical environments. The ontology includes the description of weather, road surface conditions, traffic information, road signs, signal lamps, network status, etc.(2) Ontology for car contexts. The car ontology includes three parts: the power sys

43、tem, the security system and the comfort system. The power system concerns the engine status, the accelerograph, the power (gasoline), etc. The security system includes factors related to safety of the car and the driver, such as the status of the air bag, the safe belt, the anti-lock braking system

44、 (ABS), the reverse-aids, the navigation system, and the electronic lock. The comfort system is about entertainment devices, the air conditioner, windows, etc.(3) Ontology for driver contexts. The driver contexts are about the drivers physiological conditions, including the heart beat, blood pressur

45、e, density of carbon dioxide, diameter of pupils, etc. The information is used to evaluate the health and mental statuses of the driver for determining whether he/she is able to continue driving. To use the context atoms, the subscription and publication mechanisms are employed. Those applications i

46、nterested in specific contexts atoms will be added to the subscriber list, along with information on how to publish context to them. Once a subscribed context changes, the new data will be delivered to the subscribing application.Context situation: training and recognition Context situation recognit

47、ion is a reasoning process and should be real time. This research uses a pattern-based inference engine and includes two parts:offline statistic-based situation pattern training and online situation recognition (Fig.5). The training phase is used to learn the statistical relationship between context

48、 atoms and situations and hence to generate the pattern of every single situation. The online recognition phase is used to recognize the current situation according to its pattern in the running time of a smart car. 附录B 具有情景感知的智能汽车：从模型到原型的发展摘要：由于智能汽车到处都应用着微机，所以这是有前途的领域。在敏感环境中主要就是为了智能汽车更安全和更容易的驾驶。尽管许

49、多工业创新和学术研究上取得了很大的进展，但是我们发现充分缺乏具有情景感知的智能汽车。本研究阐诉的总体结构是智能汽车的语境方面。其中一方面描述复杂的驾驶环境的模型。智能汽车原型的内置设施包括具有情景感知的软件模型和提供应用程序运行环境的硬件。对其进行评估有两个性能指标：对语境、情景识别精度和效率。对整个语境识别所的响应时间大约是一个人的1.4陪，在非时间关键型智能车的应用程序中是可接受的。关键词:智能汽车、智能车辆、环境敏感、无处不在的微机数字对象唯一标识符：10.1631/浙江大学科学杂志。A0820154文档代码:TP39 CLC.介绍 在日常生活中汽车将成为私人经常使用中重要的部分。然而，

50、他们也带来很多问题，如交通拥挤和事故。智能汽车的目的是协助驾驶员更容易驾驶，减少驾驶员的工作量和受伤的机会。为了这个目的，一个智能的汽车必须能够感知、分析、预测和反应道路的环境，智能汽车的关键特征是语境意识。 在过去的十年中已经应用许多技术，如智能交通运输系统和先进的驾驶辅助系统。然而，目前的智能汽车是不能真正感知情景。只利用在少数道路的环境类型，这被称为背景。此外，目前的智能汽车缺乏复杂的推理。这些缺点限制了辅助驾驶任务的智能车的能力和安全。本文研究的重点是如何研制出具有情景感知的智能汽车。 本文的一下部分安排如下：第二2节介绍了智能车的相关工作。在第3节对智能小车进行描述。第4节介绍综合应

51、用在智能车运行环境中具有情景感知和分析信息的模型。在第5节介绍智能汽车的原型，包括硬件设施和软件平台。在第6节和7节中给出绩效评估的结论。相关工作 在过去的十年中，许多学术界和产业界已经在研究智能汽车。以下是这一领域的主要进展的综述。（1）新的制造技术。麻省理工学院媒体实验室研制出了一个概念车，城市车（麻省理工学院，2004），一个轻量级的电动车辆。这辆车采用了完全集成在车轮的电动马达和悬挂系统，都是独立的、数字控制的、可重构的。附带无线连接和一个谷歌信息网格，司机可利用这些明确路况。（2） 汽车马努建筑中实现许多新颖的想法在他们的最新系列的概念车。宝马的互联驾驶包括宝马协助、宝马在线和司机辅

52、助系统,支持换车道警告和停车助理(Hoch et al.,2007)。梅赛德斯-奔驰是一个智能司机援助系统,利用立体摄像机和雷达传感器来监视四周车(奔驰,2007)。沃尔沃的副驾驶员是个聪明的助理,负责协调信息,研究交通状况,协助司机(沃尔沃,2007)。雷克萨斯对其LS系列提供了先进的主动的安全技术,其中包括一个先进的预碰撞系统,动态驱动、电子刹车援助,公园援助系统(雷克萨斯,2007)。（3） 防撞系统。塞维特项目开发的一个重要组成部分是监视道路，对车辆和驾驶员的状态和潜在的安全效益评价（Lee等人，2004）。智能交通车辆项目地址导航，避障和排队（Parent和Fortelle，2005

53、）。这个项目主要是在扩展时间范围，获取安全相关的信息,提高测量精度、可靠性和质量的驱动(朱里奥,2007)。（4） 车辆接口。车辆接口(助手)项目目的是最大化效率和使高级驾驶辅助系统更为安全,同时最小化工作负载和干扰车载信息系统实施的(Kutila等人,2007)。通信多媒体单元内的车(通信)项目旨在设计惩罚使用机上的多媒体人机界面。一个信息经理收集反馈信息,根据当前的驾驶和环境情况估计司机的工作量(Bellotti 等人,2005)。（5） 司机的行为识别。在一个智能汽车上，驱动程序扮演重要的角色。机器实验和动态图形模型,比如隐马尔可夫模型(Oliver 和 Pentland,2000),高

54、斯混合模型(Miyajima等人,2007年)和贝叶斯网络(Kumagai和Akamatsu,2006),可广泛应用于建模和识别司机的行为。（6）沟通和合作。Car-TALK用于传输信息在汽车附近(Reichardt等人,2002)。COM2REACT(2006)建立了一个合作的、多层次的运输车辆,车辆虚拟分中心通过沟通和车辆中心通信。COOPERS项目(2006)提供了当地情况信息,通过专门结构支持把交通、基础设施状态信息和车辆通信链接。封面项目(2006)开发语义驱动的合作系统与主要集中在基础设施和车辆之间的通信。 COVER 项目(Rusconi等人,2007)设计了一种智能合作系统,它

55、从其他车辆在附近和路边设备提供了实时信息来提高司机的反应。I-WAY项目(2006)发展合作系统防止交通事故涉及弱势道路使用者,如摩托车、自行车和行人。合作车辆基础设施系统(减振系统)项目(2006)创建了一个统一的技术解决方案允许所有车辆和基础设施元素相互沟通在一个连续的和透明的方式。7）车辆安全。安全的车载通信项目提供了一个完整的定义和实施车辆安全要求，和其他车辆间通信（Panos等人，2006）。车载Ad Hoc网络的安全也部分地解决了安全车（Magda等人, 2002; Hubaux等人, 2004; Raya和Hubaux, 2005; Bryan 和Adrian, 2005），根据

56、VANET给出了问题的陈述并提出了解决的方案。然而，我们发现大部分的工作是并没有充分语境感知。目前的工作通常集中在特殊的实用技术，如通信，传感和驱动程序的帮助。此外，为进一步分析语境的推理是不足够被重视的。这将限制智能汽车得某些配件，所以要求不同，会导致不同的智能汽车。对智能车的综合理解有一个缺乏共识和全面的观点。本研究试图从一个自底向上的方式，上下文感知的角度建立一个智能车。我们想建立一个一般性的理论基础和智能汽车的基础框架。所有可以表征的驾驶环境的实体将被收集和定义的上下文。对复杂的形势的分析、推理将发挥重要的作用。在这样一个智能车，我们可以拥有不同的服务和应用程序。整体架构 智能汽车是许

57、多不同的传感器、控制模块和执行器等的综合集成，（王，2006）。智能车的驾驶环境监测，评估可能的风险，并采取适当的行动，以避免或减少风险。（1） 交通监控。各种扫描技术可用于识别距离车和其他道路使用者之间。积极的环境感知和汽车将在不久的将来的一般能力（唐等人。，2006）。激光雷达和视觉为基础的方法可以用来提供定位信息。雷达和激光雷达传感器提供的相对位置和相对速度信息的对象。多摄像机能够消除盲点，认识障碍，并记录环境。除了上述的传感技术，汽车可以得到交通信息从互联网上或附近的汽车。（2） 驱动程序监控。司机代表最高的安全风险。几乎95%的事故是由于人的因素和几乎四分之三的人的行为是完全怪（RA

58、U，1998）。智能汽车的有前途的潜力，协助驾驶员提高态势感知和减少错误。摄像机监控驾驶员的视线和活动，智能汽车试图让驾驶员的注意力在前进的道路上。生理传感器可以检测司机是否处于良好状态。（3） 车辆监控。一辆汽车的动力学可以从机读，油门和刹车装置。这些数据将通过控制器区域网络（CAN）转移分析汽车是否功能正常。（4） 评估模块。它决定根据交通状况的驾驶任务的危险，司机和汽车。不同级别的风险会产生不同的反应，包括通知司机通过人机界面（HMI）和汽车执行器采取紧急行动。（5）人机界面。它警告说，在非紧急情况下的潜在风险的驱动程序。例如，一个累了司机会吵醒的报警声或振动座椅。视觉指示应以谨慎的方式

59、，从一个复杂的图形或长文本的句子会严重损害驾驶员的注意力和可能造成的危害。（6） 执行器。执行器将执行指定的控制对汽车没有司机的命令。智能汽车将采取积极措施，如停止的情况下，汽车的驱动程序无法正确动作，或采用被动保护减少突发事故可能造成的危害，例如，弹出安全气囊。语境感知的模型 环境监测收集汽车、司机和路面信息时，为了实现情景感知的智能车，我们必须从语境分析出发。我们开发了一个层次的情景感知的模型，这是表示和分析智能车的环境的基础上。语境感知的模型 根据抽象程度和语义，我们将语境感知分为三层：传感器层，背景层和背景层原子的情况下，传感器层的感知数据源，感知原子层作为物理世界和意义世界之间的抽象，和感知的情况下提供复杂的事实描述