GPS World, May 2017

CONNECTED CAR Sensor type Sensor name Feature Accelerometer ADXL345 Road Texture Air Quality SEN01111P Scent Barometer BMP180 Terrain Height Dust GP2Y1010AU0F Scent GNSS IMU Xsens MTi G GNSS DR Light Sensors ISL29125 Ambient Light Magnetometer Terrafix inertial compass 1000 1 Magnetic Field Microphone Phidgets1133 Road Texture Thermometer Yocto Temp Temperature Video Camera Panasonic SDR H81 Road Signs MAY 2017 WWW GPSWORLD COM GPS WORLD 23 Knowing your position is vital in an emergency vehicle and a system that incorporates a back up to GNSS would be advantageous The motivation for maintaining a continuous positioning solution is that when moving within a complex environment it is necessary to maintain the integrity of the current position In emergency situations delays are not acceptable and integrity is vital There will be no point in time when the vehicle can be delayed to obtain a position fix Although this system will be designed with emergency service vehicles such as ambulances and police cars in mind it could also be used in wider applications such as fleet management and tracking devices Ultimately crowd sourcing or cooperative techniques could be used to pool information from different vehicles equipped with the system With a very large number of vehicles maintaining the feature database the system could adapt to changes in the environment very quickly To reliably achieve meters level positioning across a range of different challenging environments a paradigm shift is needed We need to use as much information as we can cost effectively obtain from many different sources in order to determine the best possible navigation solution in terms of both accuracy and reliability This new approach to navigation and real time positioning in challenging environments requires many new lines of research to be pursued ROAD EXPERIMENT A set of sensors with a GNSS receiver were attached to a car and driven in closed loops around Stoke on Trent on multiple road types over multiple days The loops were repeated three times on each day on four road types and then repeated over three consecutive days The sensors used can be seen in TABLE 1 The accelerometer air quality sensor barometer dust sensor light sensors and microphone interfaced with an Arduino microprocessor which outputted the signals from the sensors to a laptop The Arduino sensors had a data rate of 20 measurements per second There was an individual accelerometer attached to the axel of the vehicle for use in identifying road texture There are also accelerometers that form part of the inertial measurement unit IMU and these were used for dead reckoning The onset of movement as recorded from the IMU was used to assist in identifying the beginning of each circuit During the car journeys there were two experimenters one to drive the car and another to monitor the sensors There were 5 10 minutes between each round during this time the sensors would be turned off and then restarted The equipment was designed for the outputs of the sensors to be post processed The four classes of road were suburban urban rural and high speed road The route taken and a view from Google Street View showing the general type of landscape travelled through can be seen in FIGURE 3 A road experiment travelled the routes using GPS receivers with the Arduino video camera and the IMU so that GPS time could be used as a constant for the various sensors WHOLE ROUTE ANALYSIS The outputs from the sensors were evaluated initially for their cross correlation over the whole route This process assessed whether the data from different runs over the same terrain were similar and thus had a high cross correlation FIGURE 3 Road types used in the road experiment From top left clockwise suburban high speed road rural and urban TABLE 1 Sensors used in the road experiments