SenSpeed: Sensing driving conditions to estimate vehicle speed in urban environments

Abstract

Acquiring instant vehicle speed is desirable and a corner stone to many important vehicular applications. This paper utilizes smartphone sensors to estimate the vehicle speed, especially when GPS is unavailable or inaccurate in urban environments. In particular, we estimate the vehicle speed by integrating the accelerometer's readings over time and find the acceleration errors can lead to large deviations between the estimated speed and the real one. Further analysis shows that the changes of acceleration errors are very small over time which can be corrected at some points, called reference points, where the true vehicle speed is known. Recognizing this observation, we propose an accurate vehicle speed estimation system, SenSpeed, which senses natural driving conditions in urban environments including making turns, stopping and passing through uneven road surfaces, to derive reference points and further eliminates the speed estimation deviations caused by acceleration errors. Extensive experiments demonstrate that SenSpeed is accurate and robust in real driving environments. On average, the real-time speed estimation error on local road is 1.32mph, and the offline speed estimation error is as low as 0.75mph. Whereas the average error of GPS is 3.1mph and 2.8mph respectively.

SubTrack: Enabling Real-Time Tracking of Subway Riding on Mobile Devices

“…2) Velocity Drift Calibration: In order to mitigate the velocity drift error, we develop an on-line (i.e., real-time) velocity calibration scheme based on the proposition that the accumulated drift error increases linearly over time, which has been verified by many existing studies [6,7].…”

“…Figure 4 demonstrates an example of the velocity estimation for five-stop subway ride with both our proposed real-time velocity estimation method (i.e., online) and the method in [7] (i.e., off-line). We can find the calibrated velocities from the two methods are very similar to each other, so it validates the effectiveness of our proposed method on eliminating the velocity drift.…”

“…The signals being used are either too weak to provide reliable location information or not always available along with the subway tunnels underground. An alternative way is to use built-in motion sensors in the mobile devices [7]. However, this approach cannot achieve real-time velocity drift calibration with limited reference points in the subway traveling scenario.…”

“…Existing studies [6,7] show that the accumulated drift error in inertial navigation system increases linearly over the time, which suggests that such a linear relationship could be determined if some actual information (e.g., velocity) in the middle of the subway ride is known. Fortunately, the train stops from time to time, which indicates that we can capture the stationary period (i.e., zero velocity) when it stops at each station.…”

A Survey Paper on Non Intrusive Texting While Driving Detection Using Smart Phone

“…In this paper [17], a system was proposed that the smart phone sensors are used to estimate the vehicle speed. This system is mainly useful when GPS is unavailable or irregular in Urban areas.…”