Saber Taghvaeeyan

Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement

This paper focuses on the development of a portable roadside magnetic sensor system for vehicle counting, classification, and speed measurement. The sensor system consists of wireless anisotropic magnetic devices that do not require to be embedded in the roadway-the devices are placed next to the roadway and measure traffic in the immediately adjacent lane. An algorithm based on a magnetic field model is proposed to make the system robust to the errors created by larger vehicles driving in the nonadjacent lane. These false calls cause an 8% error if uncorrected. The use of the proposed algorithm reduces this error to only 1%. Speed measurement is based on the calculation of the cross correlation between longitudinally spaced sensors. Fast computation of the cross correlation is enabled by using frequency-domain signal processing techniques. An algorithm for automatically correcting for any small misalignment of the sensors is utilized. A high-accuracy differential Global Positioning System is used as a reference to measure vehicle speeds to evaluate the accuracy of the speed measurement from the new sensor system. The results show that the maximum error of the speed estimates is less than 2.5% over the entire range of 5-27 m/s (11-60 mi/h). Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle. Vehicle length is estimated from the product of occupancy and estimated speed. The average vertical magnetic height is estimated using two magnetic sensors that are vertically spaced by 0.25 m. Finally, it is shown that the sensor system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95%. The developed sensor system is compact, portable, wireless, and inexpensive. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities, MN, USA.

Use of vehicle magnetic signatures for position estimation

This letter analyzes the magnetic signatures of cars and investigates the use of anisotropic magnetoresistive sensors to estimate the relative position of a vehicle from its magnetic signature. Theoretical analysis and experimental measurements both show that vehicle magnetic field has a first order inverse relationship with distance at small distances. However, the parameters in the magnetic field-distance relationship vary significantly with the type and size of car. A sensor system consisting of 2 magnetoresistive sensors and an extended Kalman filter can adaptively estimate these parameters in real-time. Experimental results from tests with various vehicles show that the developed sensor system can reliably estimate vehicle distance from magnetic field measurements.

The Development of Vehicle Position Estimation Algorithms Based on the Use of AMR Sensors

This paper focuses on the use of anisotropic magnetoresistive (AMR) sensors for imminent crash detection in cars. The AMR sensors are used to measure the magnetic field from another vehicle in close proximity to estimate relative position and velocity from the measurement. An analytical formulation for the relationship between magnetic field and vehicle position is developed. The challenges in the use of the AMR sensors include their nonlinear behavior, limited range, and magnetic signature levels that vary with each type of car. An adaptive filter based on the iterated extended Kalman filter (IEKF) is developed to automatically tune filter parameters for each encountered car and to reliably estimate relative car position. The utilization of an additional sonar sensor during the initial detection of the encountered vehicle is shown to highly speed up the parameter convergence of the filter. Experimental results are presented from a number of tests with various vehicles to show that the proposed sensor system is viable.

Non-Intrusive Piston Position Measurement System Using Magnetic Field Measurements

This paper proposes a novel sensing system for the non-intrusive real-time measurement of piston position inside a cylinder. The proposed sensor exploits the principle that any ferromagnetic object has an inherent magnetic field which varies as a function of position around the object. Through modeling the magnetic field as a function of position and using sensors to measure magnetic field intensity, the position of the object can be estimated. This principle is used to measure the piston position in a free piston engine without requiring any sensors inside the engine cylinder. The piston is approximated as a rectangular metallic object and the variation of the magnetic field around it is modeled. A challenge arises from the fact that the parameters of the model vary from engine to engine and are cumbersome to calibrate for each engine. This challenge is addressed by utilizing two magnetic field sensors with known longitudinal separation between them. A number of estimation methods are proposed that identify and update magnetic field parameters in real time without requiring an additional linear variable differential transformer (LVDT) sensor for calibration. The advantages and performance of these estimation methods are compared. Experimental results from a free piston engine setup show that the proposed sensor can provide 0.4 mm accuracy in position estimates. The proposed sensing concept can be utilized for piston position measurement in multi-cylinder SI and diesel engines, hydraulic cylinders, pneumatic cylinders, and in many other position measurement applications.

Two-Dimensional Sensor System for Automotive Crash Prediction

This paper focuses on the use of magnetoresistive and sonar sensors for imminent collision detection in cars. The magnetoresistive sensors are used to measure the magnetic field from another vehicle in close proximity, to estimate relative position, velocity, and orientation of the vehicle from the measurements. First, an analytical formulation is developed for the planar variation of the magnetic field from a car as a function of 2-D position and orientation. While this relationship can be used to estimate position and orientation, a challenge is posed by the fact that the parameters in the analytical function vary with the type and model of the encountered car. Since the type of vehicle encountered is not known a priori, the parameters in the magnetic field function are unknown. The use of both sonar and magnetoresistive sensors and an adaptive estimator is shown to address this problem. While the sonar sensors do not work at very small intervehicle distance and have low refresh rates, their use during a short initial time duration leads to a reliable estimator. Experimental results are presented for both a laboratory wheeled car door and for a full-scale passenger sedan. The results show that planar position and orientation can be accurately estimated for a range of relative motions at different oblique angles.

Magnetic sensor-based large distance position estimation with disturbance compensation

Magnetic sensors have previously been used for position measurement only at very small distances between the magnet and the sensor. While they could potentially also be used at larger distances by exploiting nonlinear magnetic field functions, a serious challenge comes from magnetic disturbances. The presence of foreign ferromagnetic objects, variations in the Earths magnetic field with location, and electromagnetic disturbances can cause such position estimation systems to have significant errors. This paper enables robust large-distance position estimation using redundant sensors and real-time disturbance estimation. Adaptive estimation algorithms that auto-calibrate magnetic parameters and compensate for disturbances are developed. Experimental results with a pneumatic cylinder demonstrate that sub-millimeter accuracies in position measurement can be obtained with such a system in spite of disturbances from external ferromagnetic objects. The developed sensing principle has a large number of applications, including position estimation in pneumatic cylinders, hydraulic actuators, spool valves, and many other machinery.

Nature-inspired position determination using inherent magnetic fields

Many creatures in nature, including butterflies, newts, and mole rats, use the Earths inherent magnetic field for navigation. They use magnetic field lines and variations in field intensity to determine their geographical position. This paper seeks to apply similar techniques to measure the positions of individual ferromagnetic objects found all around us in everyday life. Ferromagnetic objects have inherent magnetic fields around them. We show here that the magnetic field variation around a ferromagnetic object can be modeled using purely the geometry of the object under consideration. By exploiting this model, the position of the object can be measured quite accurately using a small inexpensive magnetic sensor. Further, the use of just one additional redundant magnetic sensor can eliminate the need to calibrate the position measurement system. As demonstrated in the paper through a series of experimental results, the developed measurement system is applicable to accurate position measurement of small and large ferromagnetic objects, including cars on highways, oscillating pistons in internal combustion engines, pneumatic cylinders, hydraulic cylinders, as well as moving parts in many machines.

Dynamic model for automotive side impact crashes

A rigid body model to represent a side impact crash is constructed using five degrees-of-freedom (dof) for the vehicle and three dof for each occupant in the vehicle. Nonlinear stiffness and damping elements and the presence of physical gaps between several components make the model highly nonlinear. The model is validated using experimental crash test data from a National Highway Traffic Safety Administration (NHTSA) database. To simplify the parameter identification process and reduce the number of parameters to be identified at each stage, a two-step process is adopted in which the vehicle is first assumed to be unaffected by the presence of the occupants, and its model parameters are identified. Subsequently, the parameters in the occupant models are identified. The active set method with a performance index that includes both the L2 and L∞ norms is used for parameter identification. A challenge is posed by the fact that the optimisation problem involved is non-convex. To overcome this challenge, a large set of random initial values of parameter estimates is generated and the optimisation method is applied with all these initial conditions. The values of parameters that provide the minimal performance index from the entire set of initial conditions are then chosen as the best parameter values. The optimal parameters values thus identified are shown to significantly improve the match between the model responses and the experimentally measured sensor signals from the NHTSA crash test.

Novel non-intrusive sensor for piston position measurement

This paper proposes a novel sensor for the non-intrusive real-time measurement of piston position inside a cylinder. The proposed sensor exploits the principle that any ferromagnetic object has an inherent magnetic field which varies as a function of position around the object. By modeling the magnetic field as a function of position and using sensors to measure magnetic field intensity, the position of the object can be estimated. This principle is used to measure piston position in a free piston engine without requiring any sensors inside the engine cylinder. The piston is approximated as a rectangular metallic object and the variation of the magnetic field around it is modeled. A challenge arises from the fact that the parameters of the model would vary from engine to engine and would be cumber-some to calibrate for each engine. This challenge is addressed by utilizing two magnetic field sensors with known longitudinal separation between them. An iterated least squares algorithm then provides adaptive parameter estimates and accurate position estimation. Experimental results from a free piston engine set up show that the proposed sensor can provide better than 0.4 mm accuracy in position estimates. The proposed sensing concept can be utilized for piston position measurement in multi-cylinder SI and diesel engines, hydraulic cylinders and in many other position measurement applications.

Two-dimensional sensor system for automotive crash prediction

This project focuses on the use of magnetoresistive and sonar sensors for imminent collision detection in cars. The magnetoresistive sensors are used to measure the magnetic field from another vehicle in close proximity, so as to estimate relative position, velocity and orientation of the vehicle from the measurement.An analytical formulation is presented for the planar variation of the magnetic field from a car as a function of two dimensional position and orientation. While this relationship itself can be used to estimate position and orientation, a challenge is posed by the fact that the parameters in the analytical function vary with the type and model of the encountered car. Since the type of vehicle encountered is not known apriori, the parameters in the magnetic field function are unknown. The use of both sonar and magnetoresisitive sensors and an adaptive estimator is shown to address this problem. While the sonar sensors do not work at very small inter-vehicle distance and have low refresh rates, their use during a short initial time duration leads to a reliable estimator. Experimental results are presented for a laboratory wheeled car door and show that planar position, relative angular position and orientation can be accurately estimated for a range of relative motions at different oblique angles.© 2012 ASME