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

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Context-aware smart car: from model to prototype

Abstract: 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 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 infrastructures 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, which 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: TP39

INTRODUCTION

Cars are becoming important private places frequently used in daily life. However, 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 environment, 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) (Küçükay and Bergholz,2004). However, current smart cars are 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 car’s ability of assisting the driving task efficiently and safely. This research 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 classification 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 researches 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 integrated in-wheel electric

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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. Automotive manu-factures implement many novel ideas in their newest series of concept cars. BMW’s 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 assistance system that utilizes stereo cameras and radar sensors to monitor the surroundings around the car (Benz, 2007).V olvo’s CoDriver is an intelligent assistant that co-ordinates information, studies the traffic situation and assists the driver (V olvo, 2007). Lexus provides advanced active safety technologies 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 SA VE-IT project develops a central component that monitors the roadway, the states of the vehicle and the 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 precision, 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 driver’s state into account (Matthias,2006).

(4) Driver-vehicle interface. The Adaptive Integrated Driver-vehicle 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 designing aneasy-to-use on-vehicle multimedia human-machine interface. An information manager collects the feed back information and estimates the driver’s workload according to the current driving and environment situation (Bellotti et al., 2005).

(5) Driver behavior recognition. The driver plays an important 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) Communication and cooperation. The Car-TALK project enables information transmitting among cars in the …… 此处隐藏：6022字，全部文档内容请下载后查看。喜欢就下载吧 ……