Previous Abstract Return to Session C1 Next Abstract

Session C1: Sensor Aiding and Augmenting

Utilizing The ACC-FMCW-Radar for Land Vehicles Navigation
Ashraf Abosekeen, Queen's University, Canada; Aboelmagd Noureldin, Royal Military College of Canada/Queen's University, Canada; Michael J. Korenberg, Queen's University, Canada
Location: Windjammer

Navigating land vehicles and self-driving cars is vital for a safe and accurate journey. Moreover, it’s essential for taking the shortest route to save fuel and protect the environment from excessive pollution. Global Navigation Satellite Systems (GNSS) such as Global Positioning System (GPS) are the prime source of navigation information for manned and unmanned vehicles. The GPS solution is accurate if there is a sufficient number of visible satellites with a direct line of sight to the GNSS receiver inside the vehicle. The presence of the line of sight (LOS) of a minimum four satellites is essential for having a complete solution. However, this is not available in all environments. There are challenging environments which prevent the GNSS receiver from getting a good navigation solution (i.e., urban areas, indoor, and tunnels). So, there is a need for a backup system during the absence of GNSS signals. Inertial Navigation System (INS) is the system of choice for this problem as INS is not affected by any signal distraction or jamming. However, INS suffers from accumulated error growth with time. These errors cause path or trajectory drift, which becomes significant in the long-term. This overtime drift keeps increasing when the INS has no measurement updates from other systems of superior accuracy. Such characteristics lead to the idea of using both GNSS and INS together to the benefit of each other during GNSS outages. GNSS/INS integration achieves a better accuracy, but such efficiency can’t be maintained during extended GNSS outages, especially with the use of low cost and commercial grade inertial sensors for INS.
Problem Statement
Urban areas with high rise buildings and structures block the GNSS signals. Such blockage occurs when the GNSS receiver can’t achieve a valid LOS connection with four satellites at a minimum. Moreover, the blockage or outage period might extend as the vehicle still moving in the same challenging environment. The GNSS/INS integration in such case had to rely only on the INS system. Nevertheless, drift in the navigation solution occurs. This drift growth with time vary from small to large as the type of the Inertial Measuring Unit (IMU) Vary from reasonable low accuracy to expensive high-end units. Relying on low-cost IMU may cause unbounded drift over time during prolonged GNSS outage.
A Reduced Inertial Sensor System (RISS) had been introduced to take the place of the complete INS with less number of sensors for land vehicle applications [1]. The RISS which consists of an odometer, forward and transversal accelerometers and one vertical gyroscope provide a complete navigation solution (Position, Velocity, and Attitude) with less mathematical operations compared with the full INS. Many modifications on the RISS applied for improving its performance such as integrating with the GPS system by enhancing the system design matrix for the integration filter [1], [2]. Moreover, an azimuth measurement update from magnetometers was added to the RISS/GPS integrated navigation system to provide azimuth update during the GPS outage periods, so the system can ensure more reliable positioning accuracy in challenging GNSS environment [3]. Also, the RISS system relies on the odometer measurements to give the land vehicle forward velocity. Unfortunately, the odometer measurements are vehicle specification’s dependent [4]. Furthermore, these speed measurements are vulnerable to several types of error sources. Such error sources can be categorized as deterministic (systematic) and non-deterministic (non-systematic). The deterministic errors that affect the odometer measurements are changes in wheel diameter due to variations in temperature, pressure, tread wear, and speed, unequal wheel diameters between the different wheels, uncertainties inefficient wheelbase (track width), and limited resolution and sample rate of the wheel encoders. The non-deterministic error sources include wheel slips, uneven road surfaces, and skidding. Both error sources are negatively affecting the velocity, traveled distance, and heading estimation using the odometer [5].
Motivation
Recently, most of the middle and high-class land vehicles are equipped with the Adaptive Cruise Control System (ACC) that uses the FM continuous wave (CW) radar. In particular, the ACC works as a warning unit for collision avoidance purposes in the driver assistant systems and is utilized for both manned and future unmanned (self-driving) vehicles. The main contributed component in the ACC is the radar unit which provides relative distance and velocity of the target (vehicle) in front [6].
Methodology
The radar unit in the ACC system mainly uses the Doppler measuring technique to measure the relative distance and velocity of the target in front. The radiation pattern of the primary radar unit is supposed to be a narrow beam pattern to avoid the other moving objects. In this paper, new functionality is added to the ACC system. Such service is using the radar unit equipped with the ACC system to measure the ground speed. Moreover, obtaining the relative velocity between the moving car and the ground is the primary goal which can be provided by the radar unit. Later on, the obtained speed is converted using the 3dB angle to get the forward speed of the carrying vehicle. The forward land vehicle velocity is much smoother, accurate and reliable than the speed obtained from the odometer because it doesn’t suffer from error sources affecting the odometer measurements. Finally, the speed measured by the radar is used instead of the odometer with a vertical gyro and two transversal accelerometers to provide a new radar-based odometry RISS system used in land vehicles navigation.
Several experiments were conducted in land vehicles in downtown environments in both Kingston and Toronto. Our vehicle is equipped with a variety of MEMS-based IMU (Crossbow, ADIS, and VTI) and a couple of GNSS receiver from Novatel and U-Blox. This paper will provide an analysis of the performance and the benefits of the forward speed measurements obtained from ACC-based radar when integrated with the above sensors. We will also discuss the gain in performance when the radar signal is combined with both accelerometers, gyroscope, and GPS.
References
[1] U. Iqbal, A. F. Okou, and A. Noureldin, “An integrated reduced inertial sensor system-RISS/GPS for land vehicles,” in IEEE/ION Position, Location and Navigation Symposium, 2008, pp. 1014–1021.
[2] M. M. Atia, T. Karamat, and A. Noureldin, “An Enhanced 3D Multi-Sensor Integrated Navigation System for Land-Vehicles,” Journal of Navigation, vol. 67, no. 4, pp. 651–671, 2014.
[3] A. Abosekeen, A. Noureldin, T. Karamat, and M. J. Korenberg, “Comparative Analysis of Magnetic-Based RISS using Different MEMS-Based Sensors,” in Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), 2017.
[4] I. Skog and P. Handel, “In-car positioning and navigation technologies; A survey,” IEEE Transaction on Intelligent Transportation Systems, vol. 10, no. 1, pp. 4–21, 2009.
[5] Z. Li, J. Wang, B. Li, J. Gao, and X. Tan, “GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications,” Journal of Navigation, vol. 67, pp. 1–17, April 2014.
[6] D. Bevly et al., “Lane Change and Merge Maneuvers for Connected and Automated Vehicles: A Survey,” IEEE Transactions on Intelligent Vehicles, vol. 1, no. 1, pp. 105–120, 2016.



Previous Abstract Return to Session C1 Next Abstract