SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments
Acquiring instant vehicle speed is desirable and a corner stone to several vital vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In specific, we estimate the vehicle speed by integrating the accelerometer’s readings over time and realize the acceleration errors will result in massive deviations between the estimated speed and the important one. Further analysis shows that the changes of acceleration errors are terribly little over time which can be corrected at some points, referred to as reference points, where the true vehicle speed can be estimated. Recognizing this observation, we have a tendency to propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments together with making turns, stopping, and passing through uneven road surfaces, to derive reference points and more eliminates the speed estimation deviations caused by acceleration errors. Intensive experiments demonstrate that SenSpeed is accurate and strong in real driving environments. On average, the $64000-time speed estimation error on native road is $two.1,mathrm km/h$ , and also the offline speed estimation error is as low as $one.21$ km/h. Whereas the average error of GPS is $5.0$ and $four.five$ km/h, respectively.
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