User requirements for the performance of Global Navigation Satellite System (GNSS)-based road applications have been significantly increasing in recent years. Safety systems based on vehicle localization, electronic fee-collection systems, and traveler information services are just a few examples of interesting applications requiring onboard equipment (OBE) capable of offering a high available accurate position, even in unfriendly environments with low satellite visibility such as built-up areas or tunnels and at low cost. In addition to that, users and service providers demand from the OBEs not only accurate continuous positioning but integrity information of the reliability of this position as well. Specifically, in life-critical applications, high-integrity monitored positioning is absolutely required. This paper presents a solution based on the fusion of GNSS and inertial sensors (a Global Positioning System/satellite-based augmentation system/inertial navigation system integrated system) running an extended Kalman filter combined with an interactive multimodel method (IMM-EKF). The solution developed in this paper supplies continuous positioning in marketable conditions and a meaningful trust level of the given solution. A set of tests performed in controlled and real scenarios proves the suitability of the proposed IMM-EKF implementation as compared with low-cost GNSS-based solutions, dead reckoning systems, single-model EKF, and other filtering approaches of the current literature.

/pdf/high-integrity-imm-ekf-based-road-vehicle-navigation-with-17netf194e.pdf

High-Integrity IMM-EKF-Based Road Vehicle Navigation With Low-Cost GPS/SBAS/INS

This research investigates real time tilting measurement using Micro-Electro-Mechanical-system (MEMS) based inertial measurement unit (IMU). Accelerometers suffer from errors caused by external accelerations that sums to gravity and make accelerometers based tilting sensing unreliable and inaccurate. Gyroscopes can offset such drawbacks but have data drifting problems. This paper presents a study on complementary and Kalman filter for tilting measurement using MEMS based IMU. The complementary filter algorithm uses low-pass filter and high-pass filter to deal with the data from accelerometer and gyroscope while Kalman filter takes the tilting angle and gyroscope bias as system states, combining the angle derived from the accelerometer to estimate the tilting angle. The study carried out both static and dynamic experiments. The results showed that both Complementary and Kalman filter were less sensitive to variations and almost no signal coupling phenomenon and able to obtain smooth and accurate results.

MEMS based IMU for tilting measurement: Comparison of complementary and kalman filter based data fusion

We present a system for collecting and analyzing information on street lighting infrastructure. We develop a car-mounted sensor platform that enables collection and logging of data on street lights during night-time drive-bys. We address several signal processing problems that are key to mapping street illumination levels, identifying street lamps, estimating their heights, and geotagging them. Specifically, we highlight an image recognition algorithm to identify street lamps from the video data collected by the sensor platform and its subsequent use in estimating the heights of street lamps. We also outline a framework to improve vehicle location estimates by combining sensor observations in an extended Kalman filter framework. Our eventual goal is to develop a semi-live virtual 3-D street lighting model at urban scale that enables citizens and decision makers to assess and optimize performance of nighttime street lighting.

Urban Street Lighting Infrastructure Monitoring Using a Mobile Sensor Platform

Urban transportation in the next few decades will shift worldwide toward electrification and automation, with the final aim of increasing energy efficiency and safety for passengers. Such a big change requires strong collaboration and efforts among public administration, research and stakeholders in developing, testing and promoting these technologies in public transportation. Working in this direction, this work provides a review of the impact of the introduction of driverless electric minibuses, for the first and last mile transportation, in public service. More specifically, this paper covers the state of the art in terms of technological background for automation, energy efficiency via electrification and the current state of the legal framework in Europe with a focus on the Baltic Sea Region.

State of the Art of Automated Buses

This paper proposes several extensive methods to predict the future location of an automobile. The goals of this paper are to find a more accurate way to predict the future location of an automobile by 3 s ahead, so that the prediction error can be greatly reduced with the innovative idea of merging global-positioning-system (GPS) data with geographic-information-system (GIS) data. The improvement starts by applying existing techniques to extrapolate the current GPS location. Comprehensive Kalman filters (KFs) are implemented to deal with inaccuracy in the different identified possible states an automobile could be found in, which are identified as constant locations, constant velocity, constant acceleration, and constant jerks. Then, the KFs are set up to be part of a interacting-multiple-model (IMM) system that provides the predicted future location of the automobile. To reduce the prediction error of the IMM setup, this paper imports an iterated geometrical error-detection method based on GIS data. The assumption that the automobile will remain on the road is made; therefore, the predictions of future locations that fall outside are corrected accordingly, making a great reduction to the prediction error. The actual experimental results validate our proposed system by reducing the prediction error to around half of what it would be without the use of GIS data.

/pdf/improving-estimation-of-vehicle-s-trajectory-using-the-3zlxts2u00.pdf

Improving Estimation of Vehicle's Trajectory Using the Latest Global Positioning System With Kalman Filtering

Autonomous vehicle navigation with standard IMU and differential GPS has been widely used for aviation and military applications. Our research interesting is focused on using some low-cost off-the-shelf sensors, such as strap-down IMU, inexpensive single GPS receiver. In this paper, we present an autonomous vehicle navigation method by integrating the measurements of IMU, GPS, and digital compass. Two steps are adopted to overcome the low precision of the sensors. The first is to establish sophisticated dynamics models which consider Earth self rotation, measurement bias, and system noise. The second is to use a sigma Kalman filter for the system state estimation, which has higher accuracy compared with the extended Kalman filter. The method was evaluated by experimenting on a land vehicle equipped with IMU, GPS, and digital compass.

http://web.cs.dal.ca/~eem/cvWeb/pubs/PZhang2005-ICIMA.pdf

J. Gu

Papers

Navigation with IMU/GPS/digital compass with unscented Kalman filter