a road is a basic requirement for some ITS applications such as traffic flow monitoring, traffic flow guidance, and traffic signal control. There are different existing ...
A DSRC-Based Traffic Flow Monitoring and Lane Detection System Nima Alam1, Asghar Tabatabaie Balaie2, Andrew G. Dempster1 1
School of Surveying & Spatial Information Systems School of Electrical Engineering & Telecommunications University of New South Wales Sydney, Australia
2
Abstract— Intelligent Transportation Systems (ITS) are developing to increase transportation efficiency and mitigate its negative impacts on society. Traffic flow monitoring, traffic guidance and traffic signal control are some of ITS applications relying on vehicle passage rate in the streets. There are already some conventional techniques for vehicle counting. In this work a novel method is proposed for vehicle passage and lane detection based on the relative acceleration between the vehicle and two anchor nodes which broadcast packets periodically using Dedicated Short Range Communication (DSRC), the nominated medium for vehicle-vehicle and vehicle-infrastructure communication. Depending on the speed of the vehicle, the performance of the proposed method is 95% or higher. Keywords- DSRC;Intelligent Transportation Systems; Traffic Flow Monitoring
I.
INTRODUCTION
Intelligent Transportation Systems (ITS) are considered to improve the quality of transportation and mitigate the negative impacts of transportation on the environment and society. Knowledge of the rate and speed of the vehicles in each lane of a road is a basic requirement for some ITS applications such as traffic flow monitoring, traffic flow guidance, and traffic signal control. There are different existing methods to measure the rate and speed of vehicles passing a monitor. Radar sensors, infrared sensors, burial inductive loops, laser sensors, and machine vision are among them [1]. Although these systems have been employed for some years, there is still research potential for some of them, especially vision-based techniques [2-5]. Mostly focusing on safety issues, Dedicated Short Range Communication (DSRC) is an emerging technology for vehicle-vehicle and vehicle-infrastructure communication accompanying ITS applications [6-8]. Although the primitive objective for vehicular communication is data sharing among the ITS entities, other applications can also be considered [9, 10]. To take full advantage of a vehicular communication platform, a real time vehicle counting and lane detection system is proposed in this work. Some advantages of the proposed method over conventional systems include insensitivity to day and night light exposure compared to vision systems, lower hardware and installation costs compared to laser scanners, and more reliability and easier installation and
maintenance than buried loops. In section II, vehicular communication and related works, based on vehicular communication, is briefly discussed. Section III, explains the problem and proposed solution. In section IV, the simulation results are discussed. Section V summarizes the contributions. II.
BACKGROUND
A. Vehicular Communication Considering the harsh vehicular environment and related communication concerns such as high level of the mobility of the nodes, multipath, and environmental dynamics caused by vehicles and pedestrians, a modified version of the Wireless Local Area Network (WLAN) protocol, IEEE802.11p has been proposed for Wireless Access in the Vehicular Environment (WAVE). A dedicated bandwidth of 75MHz in the 5.850 to 5.925 GHz band has been assigned for vehiclevehicle and vehicle-infrastructure communication by the U.S. Federal Communications Commission (FCC) [11]. Similarly, the European Telecommunications Standards Institute (ETSI) and Japanese Association of Radio Industries and Businesses (ARIB) have dedicated a similar bandwidth for such communication [12, 13]. This bandwidth is called Dedicated Short Range Communication (DSRC). DSRC channels are shared for the network nodes using Orthogonal Frequency Division Multiplexing (OFDM)[14, 15]. B.
Communication-Based Traffic Flow Monitoring In the literature, proposed traffic flow monitoring systems, which operate based on vehicular communication, mostly rely on position information of the participating vehicles being provided by Global Navigation Satellite Systems (GNSS). In [16-19] some examples of these systems are proposed. Considering the problems and shortcomings of GNSS-based positioning in urban canyons and the unavailability of this facility in covered areas such as tunnels, the technique presented in this work does not depend on the absolute position information of the vehicles. In addition to this novelty, the proposed method can determine the lane of the passing vehicle. This functionality expands the proposed system applicability for traffic flow guidance and traffic flow signaling besides traffic flow monitoring.
978-1-4244-8331-0/11/$26.00 ©2011 IEEE
III.
VEHICLE PASSAGE AND LANE DETECTION
A. Problem Definition Assume there are two beacons, with known coordinates and similar heights, on each side of a two lane street opposite each other. Figure 1 shows the situation. Without losing generality, assume that the X axis is the separating line of the lanes and the coordinate of the beacons is zero along this axis. Assume a car is travelling in one of the lanes and receives the packets broadcast by the beacons through DSRC. These packets contain position and height information of the beacons and the period of packet transmission. The problem to be solved is estimation of the lane of the vehicle when it passes between the beacons and the time of passage based on the data of the received packets from two beacons. The key to the solution for this problem is the relative acceleration difference between the vehicle and the two beacons. The proposed method can be extended to more lanes, requiring more complexity, as future work. Here, only a two lane scenario is considered.
above. Acceleration difference profiles for different speeds are shown in Figure 3. As can be seen, although the peak of the acceleration profile varies with speed, it occurs at the same location along the street. This implies that vehicle passage can be detected at somewhere fixed along the street independent of the speed. C. Relative Acceleration Estimation For estimating the relative accelerations between the vehicle and the beacons, assume that beacons are broadcasting some packets periodically with the period of Δt and the data content includes their position and height. It can be simply assumed that two beacons can broadcast over one channel of DSRC without packet collision if the packet length is small enough compared to Δt.
B. Problem Solution Based on Relative Acceleration In Figure 1, assume that the height difference between the vehicle and the beacons is h. The vehicle can calculate h, if it knows the height of beacons, with regard to its antenna height. Assuming (x,y) as the coordinate of the vehicle at time t and (xi,yi) as the coordinates of beacon i (i = 1,2). The range between the vehicle and beacon i at time t is:
ri = ( x − xi ) 2 + ( y − yi ) 2 + h 2
Figure 1. Problem definition
(1)
Assuming a low acceleration for the vehicle along the street, i.e. the X axis, and zero speed perpendicular to the street, i.e. along the Y axis, relative acceleration between the vehicle and beacon i is:
ψi =
[
d 2 ri v 2 ( y − yi ) 2 + h 2 = dt 2 ri3
]
(2) Figure 2. Acceleration difference profiles for different lanes
where v is the velocity of the vehicle along the street. The acceleration difference, Δψ=ψ1- ψ2 , is:
⎡ ( y − y1 ) 2 + h 2 ( y − y2 ) 2 + h 2 ⎤ Δψ = v 2 ⎢ − ⎥ r13 r23 ⎦ ⎣
(3)
Now, x1=x2=0 m. w is the width of street and y1= w/2 and y2 = -w/2. Also, y = w/4 when traveling in lane 1 and y = -w/4 when traveling in lane 2, using nominal values w=8 m, h=5 m, and arbitrary speed of 15 m/s for the vehicle, the acceleration difference profile is depicted in Figure 2. As can be seen, it is possible to determine the lane of the vehicle when passing through the beacons based on the pattern of the acceleration difference which hits opposite peaks for the two lanes. This pattern changes with parameters v, w, and h. For the rest of this article, w and h are considered to have the values assigned Figure 3. Acceleration difference profile for different speeds in lane 1
In the vehicle, for each received packet there is:
τ i (k ) = [TRi (k ) + δ (k ) + ζ i (k )] − [TTi (k ) + δ i (k )] − τ Pi
(4)
where k is the current epoch, τi is the signal flight time between the beacon i and the vehicle, TRi is the receive time tag in the receiver, TTi is the transmit time tag of the beacon i, δ is the current clock error of the vehicle, ζi is the receive time tag quantization error for received packet from beacon i, δi is the current clock error of the beacon i, and τPi is the total processing time for beacon i and vehicle. Assuming a constant processing time τPi, equation (4) for the previous epoch is:
τ i (k − 1) = [TRi (k − 1) + δ (k − 1) + ζ i (k − 1)] − [TTi (k − 1) + δ i (k − 1)] − τ Pi
(5)
(6)
where λ and λi are the clock drifts of the vehicle and beacon i respectively and:
⎧δ (k ) − δ (k − 1) = λΔt ⎪ (7) ⎨δ i (k ) − δ i (k − 1) = λi Δt ⎪T (k ) − T (k − 1) = Δt Ti ⎩ Ti Multiplying by c, the speed of light, in (6) and rearrangement results in: cτ i (k ) − cτ i (k − 1) ⎡ T (k ) − TRi (k − 1) − Δt ⎤ = c ⎢ Ri ⎥⎦ Δt Δt (8) ⎣ c + c(λ − λi ) + (ζ i (k ) − ζ i (k − 1)) Δt Assuming small Δt, for example of 10 ms, over which vehicle speed is constant, the left side of the equation (8) represents the range-rate between the beacon i and the vehicle:
⎡TRi (k ) − TRi (k − 1) − Δt ⎤ ⎥⎦ + c(λ − λi ) Δt ⎣ (9) c + (ζ i (k ) − ζ i (k − 1)) Δt From equation (9), the acceleration between the vehicle and beacon i is:
ωi (k ) = c ⎢
ψ i (k ) =
ωi (k ) − ωi ( k − 1)
= Δt ⎡ T ( k ) − 2TRi ( k − 1) + TRi ( k − 2) ⎤ c ⎢ Ri ⎥⎦ Δt 2 ⎣
(11)
⎡ ζ ( k ) − 2ζ i ( k − 1) + ζ i ( k − 2) ⎤ ei ( k ) = c ⎢ i ⎥⎦ Δt 2 ⎣
(12)
As can be seen, the estimation error is a combination of receiver time tag quantization errors over the last three epochs. The important point in equation (12) is the increasing error with decreasing period of packet transmission, Δt. However, any increase of Δt for noise mitigation causes loss of acceleration tracking for fast dynamics. The estimate of the acceleration difference is:
Δψˆ = ψˆ1 (k ) − ψˆ 2 (k )
Subtracting (5) from (4) results in:
τ i (k ) − τ i (k − 1) = TRi (k ) − TRi (k − 1) + λΔt − Δt − λi Δt + ζ i ( k ) − ζ i (k − 1)
⎡ TRi ( k ) − 2TRi ( k − 1) + TRi ( k − 2) ⎤ ⎥⎦ Δt 2 ⎣ and estimation error is:
ψˆ i ( k ) = c ⎢
(10)
⎡ ζ (k ) − 2ζ i (k − 1) + ζ i ( k − 2) ⎤ + c⎢ i ⎥⎦ Δt 2 ⎣ As can be seen, the effect of clock drifts is eliminated in Eq. (10) if drift is assumed to have a constant rate over such a short interval. Regarding this equation, the relative acceleration between the vehicle and beacon i can be estimated by:
(13)
For a sample estimate of acceleration difference with equation (13), assume v=15 m/s and Δt=10 ms. Considering 3 Mb/s for DSRC bit rate and 200 bytes per packet , the length of each packet will be less than 1 ms. This is small enough to allow packet broadcast with 10 ms intervals. Also assume that a vehicle is within range of the beacons at a distance of 100 m in lane 1 (x= -100 m, y=2 m) and continues in the same lane to x=100 m and y=2 m. Receive time tag quantization error is considered 1/180 µs. This amount is set with regard to MK2 WAVE-DSRC radio from Cohda WirelessTM [20].Figure 4 shows the estimated acceleration differences over the vehicle travel time. As can be seen, the estimated acceleration difference is very noisy and the actual pattern, depicted in Figure 2, which is expected to happen when passing the beacons, at t=6.67 s, is lost in the estimated acceleration difference. D. Detection of the Lane and Vehicle Passage based on Estimated Acceleration Difference For extracting the acceleration difference pattern, or equivalently detecting the vehicle passage, we define the correlation function between the expected pattern and estimated acceleration difference. The following correlation function is defined:
G (t ) =
K v4
t
t
∫ α∫ β (vτ − vt + α α
t−
v t−
2) Δψˆ (τ )d 2τ
(14)
v
where α is the length of the street over which the correlation window is defined, t is current time, 1/v4 is used for normalization of the G(t) for different speeds, K is an arbitrary constant to achieve a certain peak for G(t), β is the correlating pattern:
⎧Δψ ( x) , − α 2 ≤ x ≤ α 2 (15) 0 , otherwise ⎩ where Δψ is defined by equation (3). Figure 5 shows β for different speeds. Double integration in equation (14) helps mitigate noise. Assuming β is a dimensionless entity, K=105 m3/s4, α=10 m, and apply the estimated acceleration of Figure 4 to Equation (14), Figure 6 shows the result. As can be seen, the acceleration difference pattern is detected by the proposed correlating method. Considering Figure 2, if the vehicle travels
β ( x) = ⎨
in lane 2, the correlation function hits the negative peak. This can be used for lane detection. The correlation peak is independent of v but depends on α, w, h, and K. Knowing these parameters, the maximum of G(t) can be calculated by replacing Equation (3) in Equation (14) instead of estimated acceleration difference.
Having this maximum, a threshold for peak detection can be defined. The latency of this method is important. Regarding Figure 6, the correlation peak occurs with a delay after passing the beacons which is equivalent to some displacement on the street, or X axis, and we call this xd. Knowing this displacement is important for instant position estimation of the vehicle when it passes between the beacons. Once this displacement is known, the position of the vehicle can be estimated based on the position of the beacons which is broadcast by them. xd is the point that G(t) is maximum. For calculating xd, the differential of G(t) with ideal acceleration difference , Equation (3), must be zero. This with a variable conversion from time to position along the X axis results in: xd
∫ Δψ ( x)Δψ ( x − x
d
+ α 2)dx = 0
(16)
xd −α
and the smallest positive root is xd. It is obvious from Equation (16) that displacement xd does not depend on the speed of the vehicle and solving this equation leads to xd= α, i.e. displacement is always equal to correlation window size. IV. Figure 4. Estimated acceleration difference
Figure 5. Correlating pattern
Figure 6. Ideal and estimated correlation function for the signal in Figure 4
SIMULATION AND RESULTS
The tool which was used in this simulation is MATLAB version 7.9.0.529(R2009b). For simulating the proposed method, a two lane street with lane width of 4 m and beacons height of 5 m are considered. It is assumed that beacon positions and street or lane width are transmitted to the vehicle by the beacons. The packet broadcast period, Δt, is assumed to be 10 ms and α is set to 9.1366 m. This specific value of α is chosen in such a way that leads to β(x)≥0. Receiver time tag quantization error is 1/180 µs. Constant K is set to 105 m3/s4to bring the correlation peak to an order of magnitude below the arbitrary limit of 100. The maximum of G(t) within this situation is calculated, 86.52, and ±80% of this amount is considered for threshold of peak detection. The performance of the algorithm is evaluated for different speeds from 10 to 110 kph. For each speed, 5000 trials are simulated. For each trial, the vehicle clock drift is considered as a random number between -20 and +20 ppm. The error in the odometer-based speed is considered as a zero mean Gaussian random variable with Standard Deviation (STD) of 1 m/s. Also it is assumed that the vehicle has a random smooth movement along the street, within its lane during the trip. A moving average of odometer-based measured speed is used as the current speed of the vehicle. Figure 7 shows the vehicle passage detection rate and true lane detection rate. As can be seen, the vehicle detection rate is almost constant and about 95% for different speeds. This rate could be increased by lowering the threshold assigned for correlation peak detection at the cost of decreasing the accuracy of estimated passage time. The lane of the vehicle is almost always detected truly for lower speeds but for the speeds over 40 kph , the performance begins to decline. The estimate of the vehicle position when the correlation peak detected is xd= α along the street. Due to the estimated acceleration error and odometer noise, the estimated vehicle detection time is erroneous and as a result, the estimated position of the passing vehicle is not its real position. Figure 8 shows the STD of the error of estimated position along the street when the vehicle passes through the beacons. As can be
seen, the estimated position error increases when speed is higher. V.
CONCLUSION
A vehicle counting and lane detection technique is proposed. The presented technique works based on DSRC packets broadcast by two beacons with a known fixed period. The proposed method relies on time tags of received packets. The beacons periodically broadcast packets including the required data for the vehicle. The main process is carried out in the vehicle. The vehicle estimates its lane and passage time. The result can be transmitted in one packet to a beacon or a control center near the beacons for ITS purposes. The main contributions of this work are i) a new approach for traffic flow monitoring ii) instant lane detection useful for some applications such as traffic guidance and traffic signal control. The estimated position of the vehicle, when passing through the beacons, can be used for positioning enhancement purposes. The advantage of this method is implementation on a vehicular communication platform. A method for routes with more lanes, improving the lane detection rate for high speeds, and position tracking systems based on detected lane are considered as future work.
Figure 7. Vehicle detection rate for different speeds
Figure 8. STD of estimated position of the passing vehicle
VI.
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