Nowadays, numerous journals and conferences have published articles related to context-aware systems, indicating many researchers' interest. Therefore, the goal of this paper is to review the works that were published in journals, suggest a new classification framework of context-aware systems, and explore each feature of classification framework. This paper is based on a literature review of context-aware systems from 2000 to 2007 using a keyword index and article title search. The classification framework is developed based on the architecture of context-aware systems, which consists of the following five layers: concept and research layer, network layer, middleware layer, application layer and user infrastructure layer. The articles are categorized based on the classification framework. This paper allows researchers to extract several lessons learned that are important for the implementation of context-aware systems.

Context-aware systems

Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.

Land-Use Classification Using Taxi GPS Traces

| All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

In this paper we present a design space for interactive windshield displays in vehicles and discuss how this design space can support designers in creating windshield applications for drivers, passengers, and pedestrians. Our work is motivated by numerous examples in other HCI-related areas where seminal design space papers served as a valuable basis to evolve the respective field -- most notably mobile devices, automotive user interfaces, and interactive public displays. The presented design space is based on a comprehensive literature review. Furthermore we present a classification of 211 windshield applications, derived from a survey of research projects and commercial products as well as from focus groups. We showcase the utility of our work for designers of windshield applications through two scenarios. Overall, our design space can help building applications for diverse use cases. This includes apps inside and outside the car as well as applications for specific areas (fire fighters, police, ambulance).

A Design Space to Support the Development of Windshield Applications for the Car

This paper demonstrates a novel car-following model focused on passenger comfort, for example, a rapid deceleration will make passengers uncomfortable. The brake comfort model of car following was set up according to the relationship between vehicle deceleration and passenger comfort levels. The model calculates the controlled car's acceleration by measuring the distance between the controlled car and its preceding car, as well as the velocity of the controlled car. By controlling the car's acceleration, the model is able to keep riders feeling comfortable. The friction coefficient between the car and the road surface is also considered. Experiments show that the model is highly compatible with real cases.

A Smart Car Control Model for Brake Comfort Based on Car Following

Compared with smart spaces addressed previously, the smart vehicle space is quite special. First, unlike room, it is a high mobile space. Second, it requires frequent information exchange with the outer environment; for instance, it may need local traffic information and other local services. The complex vehicle space needs a software infrastructure of high adaptation to meet the complex and easy variational situations. This paper proposes a semantic and adaptive middleware platform, i.e., ScudWare, for smart vehicle space. In ScudWare, techniques of multiagent, context-aware, and adaptive component management are smoothly synthesized. It consists of three key components, namely 1) semantic virtual agents; 2) a semantic context management service; and 3) an adaptive component management service. This enables entities in smart vehicle space to interact autonomously, provides semantic-integration context awareness, and supports component-based adaptability and scalability. A test bed of smart car space is built to evaluate the ScudWare, which demonstrates the effectiveness and efficiency of ScudWare.

ScudWare : A semantic and adaptive middleware platform for smart vehicle space

Compared with smart spaces addressed previously, the smart vehicle space is quite special. First, unlike room, it is a high mobile space. Second, it requires frequent information exchange with the outer environment; for instance, it may need local traffic information and other local services. The complex vehicle space needs a software infrastructure of high adaptation to meet the complex and easy variational situations. This paper proposes a semantic and adaptive middleware platform, i.e., ScudWare, for smart vehicle space. In ScudWare, techniques of multiagent, context-aware, and adaptive component management are smoothly synthesized. It consists of three key components, namely 1) semantic virtual agents; 2) a semantic context management service; and 3) an adaptive component management service. This enables entities in smart vehicle space to interact autonomously, provides semantic-integration context awareness, and supports component-based adaptability and scalability. A test bed of smart car space is built to evaluate the ScudWare, which demonstrates the effectiveness and efficiency of ScudWare

ScudWare: A Semantic and Adaptive Middleware Platform for Smart Vehicle Space

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.

Jie Sun

Papers

ScudWare : A semantic and adaptive middleware platform for smart vehicle space

ScudWare: A Semantic and Adaptive Middleware Platform for Smart Vehicle Space

Context-aware smart car: from model to prototype