具有情景感知的智能汽車：從模型到原型的發(fā)展畢業(yè)論文外文翻譯

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. 附錄b 具有情景感知的智能汽車：從模型到原型的發(fā)展摘要：由于智能汽車到處都應用著微機，所以這是有前途的領域。在敏感環(huán)境中主要就是為了智能汽車更安全和更容易的駕駛。盡管許

49、多工業(yè)創(chuàng)新和學術研究上取得了很大的進展，但是我們發(fā)現(xiàn)充分缺乏具有情景感知的智能汽車。本研究闡訴的總體結構是智能汽車的語境方面。其中一方面描述復雜的駕駛環(huán)境的模型。智能汽車原型的內(nèi)置設施包括具有情景感知的軟件模型和提供應用程序運行環(huán)境的硬件。對其進行評估有兩個性能指標：對語境、情景識別精度和效率。對整個語境識別所的響應時間大約是一個人的1.4陪，在非時間關鍵型智能車的應用程序中是可接受的。關鍵詞:智能汽車、智能車輛、環(huán)境敏感、無處不在的微機數(shù)字對象唯一標識符：10.1631/浙江大學科學雜志。a0820154文檔代碼:tp39 clc.介紹 在日常生活中汽車將成為私人經(jīng)常使用中重要的部分。然而，

50、他們也帶來很多問題，如交通擁擠和事故。智能汽車的目的是協(xié)助駕駛員更容易駕駛，減少駕駛員的工作量和受傷的機會。為了這個目的，一個智能的汽車必須能夠感知、分析、預測和反應道路的環(huán)境，智能汽車的關鍵特征是語境意識。 在過去的十年中已經(jīng)應用許多技術，如智能交通運輸系統(tǒng)和先進的駕駛輔助系統(tǒng)。然而，目前的智能汽車是不能真正感知情景。只利用在少數(shù)道路的環(huán)境類型，這被稱為背景。此外，目前的智能汽車缺乏復雜的推理。這些缺點限制了輔助駕駛任務的智能車的能力和安全。本文研究的重點是如何研制出具有情景感知的智能汽車。 本文的一下部分安排如下：第二2節(jié)介紹了智能車的相關工作。在第3節(jié)對智能小車進行描述。第4節(jié)介紹綜合應

51、用在智能車運行環(huán)境中具有情景感知和分析信息的模型。在第5節(jié)介紹智能汽車的原型，包括硬件設施和軟件平臺。在第6節(jié)和7節(jié)中給出績效評估的結論。相關工作 在過去的十年中，許多學術界和產(chǎn)業(yè)界已經(jīng)在研究智能汽車。以下是這一領域的主要進展的綜述。（1）新的制造技術。麻省理工學院媒體實驗室研制出了一個概念車，城市車（麻省理工學院，2004），一個輕量級的電動車輛。這輛車采用了完全集成在車輪的電動馬達和懸掛系統(tǒng)，都是獨立的、數(shù)字控制的、可重構的。附帶無線連接和一個谷歌信息網(wǎng)格，司機可利用這些明確路況。（2） 汽車馬努建筑中實現(xiàn)許多新穎的想法在他們的最新系列的概念車。寶馬的互聯(lián)駕駛包括寶馬協(xié)助、寶馬在線和司機輔

52、助系統(tǒng),支持換車道警告和停車助理(hoch et al.,2007)。梅賽德斯-奔馳是一個智能司機援助系統(tǒng),利用立體攝像機和雷達傳感器來監(jiān)視四周車(奔馳,2007)。沃爾沃的副駕駛員是個聰明的助理,負責協(xié)調信息,研究交通狀況,協(xié)助司機(沃爾沃,2007)。雷克薩斯對其ls系列提供了先進的主動的安全技術,其中包括一個先進的預碰撞系統(tǒng),動態(tài)驅動、電子剎車援助,公園援助系統(tǒng)(雷克薩斯,2007)。（3） 防撞系統(tǒng)。塞維特項目開發(fā)的一個重要組成部分是監(jiān)視道路，對車輛和駕駛員的狀態(tài)和潛在的安全效益評價（lee等人，2004）。智能交通車輛項目地址導航，避障和排隊（parent和fortelle，2005

53、）。這個項目主要是在擴展時間范圍，獲取安全相關的信息,提高測量精度、可靠性和質量的驅動(朱里奧,2007)。（4） 車輛接口。車輛接口(助手)項目目的是最大化效率和使高級駕駛輔助系統(tǒng)更為安全,同時最小化工作負載和干擾車載信息系統(tǒng)實施的(kutila等人,2007)。通信多媒體單元內(nèi)的車(通信)項目旨在設計懲罰使用機上的多媒體人機界面。一個信息經(jīng)理收集反饋信息,根據(jù)當前的駕駛和環(huán)境情況估計司機的工作量(bellotti 等人,2005)。（5） 司機的行為識別。在一個智能汽車上，驅動程序扮演重要的角色。機器實驗和動態(tài)圖形模型,比如隱馬爾可夫模型(oliver 和 pentland,2000),高

54、斯混合模型(miyajima等人,2007年)和貝葉斯網(wǎng)絡(kumagai和akamatsu,2006),可廣泛應用于建模和識別司機的行為。（6）溝通和合作。car-talk用于傳輸信息在汽車附近(reichardt等人,2002)。com2react(2006)建立了一個合作的、多層次的運輸車輛,車輛虛擬分中心通過溝通和車輛中心通信。coopers項目(2006)提供了當?shù)厍闆r信息,通過專門結構支持把交通、基礎設施狀態(tài)信息和車輛通信鏈接。封面項目(2006)開發(fā)語義驅動的合作系統(tǒng)與主要集中在基礎設施和車輛之間的通信。 cover 項目(rusconi等人,2007)設計了一種智能合作系統(tǒng),它從其他車輛在附近和路邊設備提供了實時信息來提高司機的反應。i-way項目(2006)發(fā)展合作系統(tǒng)防止交通事故涉及弱勢道路使用者,如摩托車、自行車和行人。合作車輛基礎設施系統(tǒng)(減振系統(tǒng))項目(2006)創(chuàng)建了一個統(tǒng)一的技術解決方案允許所有車輛和基礎設施元素相互溝通在一個連續(xù)的和透明的方式。待添加的隱藏文字內(nèi)容27）車輛安全。安全的車載通信項目提供了一個完整的定義和實施車輛安全要求，和其他車輛間通信（panos等人，2006）。車載ad hoc網(wǎng)絡的安全也部分地解決了安全車（magda等人, 2002; hubaux等人, 2004; raya和hubau