Abstract
Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. The authors propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) channel state information instead of received signal strength indicator (RSSI) is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80 cm when vehicle transmission period is 100 ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency.
Highlights
On the roads dominated by high-speed vehicles, pedestrians are susceptible to injury or even death in the collisions with vehicles, and are known to be ‘weak in traffic’
We selected support vector machine with the radial basis function (RBF) kernel to train a classifier to predict from channel state information (CSI) information whether a signal contains an line of sight (LOS) path or not
We selected support vector regression (SVR) with the RBF kernel to train a model for estimating distance from CSI, using all CSI data with LOS paths
Summary
On the roads dominated by high-speed vehicles, pedestrians are susceptible to injury or even death in the collisions with vehicles, and are known to be ‘weak in traffic’. The LOS path might be obstructed by roadside buildings, and a GNSS receiver receives a reflected signal instead This results in a large error in measured range, which cannot be well removed by augmentation techniques such as differential global positioning system and remains the largest error source in urban areas. In [11], a method is suggested to integrate 3D map (for detecting LOS path of satellite signals), inertial measurement unit (IMU) (for measuring moving direction), speedometer and camera-based lane detection via a particle filter. This method achieves an average positioning error of 0.75 m in the urban area of Tokyo, its instantaneous error may be as large as 3 m.
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