Signal Strength and Sensing System Configuration
Velocity Estimation
Right-turn Detection
Vehicle Classification
• AMR-3 is placed 0.9 meters from AMR-1 and these two sensors are used for vehicle velocity estimation.
• The relatively short distance between the sensors removes the problem due to a vehicle performing a maneuver which may be detected only by one of the two sensors.
• A reliable method to calculate the time delay between the two sensor locations is by using the cross-correlation between the sensors signals.
• The time delay in terms of samples is given by 𝑛𝑑 = argmax𝑛𝑓 𝑛
𝑓 𝑛 = 𝐵1𝑚𝑎𝑔 𝑚 𝐵3𝑚𝑎𝑔 𝑚− 𝑛
𝑁−1
𝑚=0
− 𝑁 − 1 ≤ 𝑛 ≤ 𝑁 − 1
• DSP techniques are used to reduce the computational effort.
• A test vehicle equipped with carrier-phase GPS has been used to verify the accuracy of the proposed velocity estimation method.
• The velocity estimates are multiplied by the factor
𝑐 = min(𝐵𝑖𝑛𝑡−1
𝐵𝑖𝑛𝑡−3,𝐵𝑖𝑛𝑡−3
𝐵𝑖𝑛𝑡−1) to account for misalignment
of the sensors and get zero-offset estimates.
Portable Roadside Sensors for Vehicle Counting, Classification and Speed Measurement Saber Taghvaeeyan and Rajesh Rajamani
Introduction Robustness to Traffic on the Non-adjacent Lane
• A portable sensor system is designed that can be placed adjacent to the road and can be used for vehicle counting, speed measurements and vehicle classification.
• The sensor system consists of magnetoresisitve devices that measure magnetic field and associated signal processing algorithms.
• The sensor system can make these traffic measurements reliably for traffic in the lane adjacent to the sensors. The vehicle detection rate accuracy is 99%.
• The developed signal processing algorithms enable the sensor to be robust to the presence of traffic in other lanes of the road.
• The velocity estimation has a max error of 2.5% over the entire speed range 5 – 60 mph.
• Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle.
• The sensor system can be used to reliably count the number of right-turns at an intersection.
• The developed sensor system is compact, portable, wireless and inexpensive.
• Signals from 216 vehicles driving in the non-adjacent lane were also recorded.
• Passengers vehicles driving in the non-adjacent lane typically do not create detection errors.
• However, larger vehicles (trucks, buses, etc.) in the non-adjacent lane may create large enough signals to cause over-detection and affect accuracy of the system.
• 15 vehicles out of 216 vehicles created a large enough signal to be miscounted as vehicles passing in the adjacent lane.
• If uncorrected, this will cause an over-detection error of 8%.
• Similar error rates (7-15%) have been reported in literature even for magnetic sensors placed in the middle of the lane.1
Use of AMR-2 to reject errors due to traffic passing in the non-adjacent lane
• It is shown that the magnetic field intensity around a vehicle has a relation that approximately varies as 1/𝑥 with distance, where 𝑥 is the distance from the vehicle.2
• Hence, the ratio 𝐵2
𝐵1 should be larger for vehicles in the non-adjacent lane, compared to
vehicles passing in the adjacent lane.
• Also the vehicles passing in the non-adjacent lane have a much lower peak value, 𝐵𝑚𝑎𝑥, on average compared to vehicles passing in the adjacent lane.
• These two metrics can be used to reject the traffic passing in the non-adjacent lane affecting the sensors.
• The following figure shows the result of applying the proposed method to the data set.
• A Support Vector Machine has been used to come up with the classification boundary.
• Using the proposed method, the error reduces from 8% to 1%.
1 J. Medina, A. Hajbabaie and R. Benekohal. Detection performance
of wireless magnetometers at signalized intersection and railroad
grade crossing under various weather conditions. Transportation Research Record, pp. 233-241. 2011.
2 S. Taghvaeeyan and R. Rajamani. Use of vehicle magnetic signatures for position estimation. Applied Physics
Letters 99(13), pp. 134101-134101-3. 2011
• Knowing the time duration and velocity of each passing vehicle, the magnetic length of the vehicle can be calculated and used for vehicle classification.
• Vehicles are divided into four classes, Class 1: Sedans, Class 2: SUVs, Vans and Pickups, Class 3: Buses and 2,3-axle Trucks and Class 4: Articulated Buses and 4,5-axle Trucks.
• Since vehicles in class I and class II have similar length and consequently similar magnetic lengths, it is not possible to classify them by using only magnetic length.
• It is expected that magnetic component locations of a vehicle in Class II lead to a higher magnetic height compared to vehicles in Class I.
• Placing another sensor, AMR-4, one foot vertically
above AMR-1, it is expected that the ratio 𝐵𝑧−4
𝐵𝑧−1
will be larger for vehicles in Class II.
• This ratio along with the magnetic length can be used to determine boundaries for classifying Class I and Class II vehicles with an accuracy of 83%.
• Using just one AMR sensor as shown, the number of right-turns at an intersection can be counted. During the experiments, 56 out of 59 right-turns were counted correctly resulting in a detection rate of 95%.
• Typically straight-driving vehicles are not detected, since they pass at a larger distance from the sensor compared to vehicles making a right turn.
• However larger straight-driving vehicles can create large enough signals to be miscounted as vehicles making right turns.
• During the experiments, 18 straight driving vehicles created large enough signals to be miscounted as right-turning vehicles which results in a detection error of 31%, if uncorrected.
• Two methods, A and B, are proposed to identify and reject the errors caused by straight driving vehicles, using two and four AMR sensors respectively.
• Considering the shown sensor configuration, integrating the signals from 4 AMR sensors of each detected vehicle we expect the following
• Method A: The ratio 𝑟 =𝐵𝑖𝑛𝑡−2
𝐵𝑖𝑛𝑡−3 should be closer to 1 for straight driving vehicles since
they pass at larger distances from the sensors.
• Method B: A plane is fit to the measurements from the four AMR sensor. By considering the angle of the plane, 𝛾, the straight-driving vehicles can be excluded.
• The two methods can be used separately or combined. With classification boundaries, straight- driving vehicles can be completely excluded reducing the 31% misdetection error to zero.
: AMR Sensors
1 3
2 4
Scenario 1: Straight on Lane 1
Scenario 2: Right turn from Lane 1 to Lane 2
Scenario 3: Straight on Lane 2
d = 20 cm
x
yz
1 2 3 4
Scenario 1: 𝐵𝑖𝑛𝑡−1 ≅ 𝐵𝑖𝑛𝑡−3 > 𝐵𝑖𝑛𝑡−2 ≅ 𝐵𝑖𝑛𝑡−4
Scenario 2: 𝐵𝑖𝑛𝑡−3 > 𝐵𝑖𝑛𝑡−1 ≅ 𝐵𝑖𝑛𝑡−4 > 𝐵𝑖𝑛𝑡−2
Scenario 3: 𝐵𝑖𝑛𝑡−3 ≅ 𝐵𝑖𝑛𝑡−4 > 𝐵𝑖𝑛𝑡−1 ≅ 𝐵𝑖𝑛𝑡−2
• The 3-axis HMC2003 set of AMR devices from Honeywell are utilized.
• The signal levels are typically 10 times smaller when sensors are placed adjacent to the road compared to the case when sensors are placed on-road in the center of the lane.
• Sensors outputs are amplified to get better signal-to-noise ratio and for use of the signals for vehicle counting, speed measurement and classification.
• The following figure shows the configuration of the sensing system.
• AMR sensors 1 and 2 are used to obtain an estimate of lateral location of the vehicle.
• AMR sensors 1 and 3 are used to calculate the longitudinal velocity of the vehicle.
• AMR sensors 1 and 4 are used to get a rough estimate of the average vertical magnetic height of passing vehicles.
Vehicle Detection and Counting
• Magnetic readings of the Z axis of AMR-1 are used for detecting and counting the passing vehicles in the adjacent lane.
• A threshold of 30 counts was used as the vehicle detection threshold.
• Signals from 188 vehicles driving in the adjacent lane were recorded, 186 vehicles created a large enough signal to be detected resulting in a detection rate of 99%.
5.5 6 6.5 7 7.5 8-150
-100
-50
0
50
100
150
200
250
300
350Magnetic Field Readings - Ford Ranger - Sensor on the Road
time (sec)
B (
co
un
ts)
Bx
By
Bz
9 9.5 10 10.5 11 11.5 12 12.5 13-25
-20
-15
-10
-5
0
5
10
15
20Magnetic Field Readings - Ford Ranger - Side of the Road
time (sec)
B (
co
un
ts)
Bx
By
Bz
0 50 100 150 2000.85
0.9
0.95
1
B1-max
(counts)
B2/B
1
Adj Lane
Non-adj Lane
Class. Bound.
5 10 15 20 25 30-15
-10
-5
0
5
10Velocity Estimation Error
GPS Velocity (m/s)
Err
or
(%)
Threshold Method
Cross-corr. Method
Class I Class II Class III Class IV0
5
10
15
20
25
Magnetic L
ength
(m
)
2 4 6 8 10
0.8
1
1.2
1.4
1.6
1.8
2
Magnetic length (m)
B4-z
/ B
1-z
Class I
Class II
-20 0 20 40 60 80 100 12055
60
65
70
75
80
85
90
95
(degrees)
r (%
)
Right turn
Straight on Lane 1
Straight on Lane 2
1 3
2: AMR Sensors1 2 3
90 cm
10 cm44
AMR SensorsSide Walk
Lane 1
Lane 2
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