Abstract

Modern vehicles are enhanced with increased computation, communication and sensing capabilities, providing a variety of new features that pave the way for the deployment of more sophisticated services. Specifically, smart cars employ hundreds of sensors and electronic systems in order to obtain situational and environmental information. This rapid growth of on-vehicle multi-sensor inputs along with off-vehicle data streams introduce the smart car era. Thus, systematic techniques for combining information provided by on- and off-vehicle car connectivity are of remarkable importance for the availability and robustness of the overall system. This paper presents a new method to employ service oriented agents that cohesively align on- and off-vehicle information in order to estimate the current status of the car. In particular, this work combines, integrates, and evaluates multiple information sources targeting future smart cars. Specifically, the proposed methodology leverages weather-based, on-route, and on-vehicle information. As a use case, the presented work informs the driver about the recommended speed that the car should adapt to, based on the current status of the car. It also validates the proposed speed with real-time vehicular measurements.

Highlights

  • Modern high-end vehicles are equipped with up to 100 embedded Electronic Control Units (ECUs), each executing standard services with pre-defined inputs [1,2]

  • Advanced driver-assistant systems (ADAS) and multimedia applications are some examples of services that are realized by running complex algorithms on embedded ECUs [3]

  • Radar, lidar, GPS, are some of the types of sensors which are deployed in modern vehicles and create input streams that are processed by the ECUs when running complex algorithms for sophisticated services, such as cruise control or forward-collision warning [5,6]

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Summary

Introduction

Modern high-end vehicles are equipped with up to 100 embedded Electronic Control Units (ECUs), each executing standard services with pre-defined inputs [1,2]. Advanced driver-assistant systems (ADAS) and multimedia applications are some examples of services that are realized by running complex algorithms on embedded ECUs [3]. Those pre-specified functions cannot be enhanced throughout the traditional vehicle architecture, since it is very expensive to develop new services on the current static vehicle architecture [4]. Radar, lidar, GPS, are some of the types of sensors which are deployed in modern vehicles and create input streams that are processed by the ECUs when running complex algorithms for sophisticated services, such as cruise control or forward-collision warning [5,6]

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