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