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(2)

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 foundation 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 applications 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 actions 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 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, recognize 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-quarters 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 driver’s gaze and activity, smart cars attempt to keep the driver’s attention on the road 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

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the car functions normally.

(4)Assessment 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.

(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 driver’s attention and possibly cause harm.

(6)Actuators. The actuators will execute specified control on the car without the driver’s 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 possible 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, which 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 sensor 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 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 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 system, 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 (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 driver’s physiological

conditions, including the heart beat, blood pressure, 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.

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Those applications interested 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 t …… 此处隐藏：5317字，全部文档内容请下载后查看。喜欢就下载吧 ……