A Petri-Net Based Context Representation in Smart Car Environment

A Petri-Net Based Context Representation in Smart Car Environment

Jie Sun (Ningbo University of Technology, China), Yongping Zhang (Ningbo University of Technology, China) and Jianbo Fan (Ningbo University of Technology, China)
Copyright: © 2011 |Pages: 13
DOI: 10.4018/jhcr.2011040103
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Driving is a complex process influenced by a wide range of factors, especially complex interactions between the driver, the vehicle, and the environment. This paper represents the complex situations in smart car domain. Unlike existing context-aware systems which isolate one context situation from another, such as road congestion and car deceleration, this paper proposes a context model which considers the driver, vehicle and environment as a whole. The paper tries to discover the inherent relationship between the situations in the smart car environment, and proposes a context model to support the representation of situations and their correlation. The detailed example scenarios are given to illustrate our idea.
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2. Smart Car

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 Figure 1.

Figure 1.

The general architecture of a smart car


The information, i.e., context, needing to be collected for a smart car includes:

  • 1.

    Traffic situation: A variety of scanning technologies are 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 near future (Tang, Wang, & Miao, 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 Internet or nearby cars.

  • 2.

    Driver situation: 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 situation: 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) in order to analyze whether the car functions normally.

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