OMNIVIEW : A COLLABORATIVE S YSTEM FOR ASSISTING DRIVERS WITH A MAP OF SURROUND T RAFFIC
B. Distance Estimation
In order to form a useful map for improving safety, besides the correctness of the vehicle information, the distance between the vehicles should also be estimated correctly.
To assess the accuracy of distance estimation by OmniView, we took pictures of 10 vehicles, with four phones (Galaxy S4, iPhone 4S and two Galaxy Nexus) from 3 directions (left, right, and rear) and different distances (ranging from 1 to 50 meters). We kept the vehicles stationary in this preliminary evaluation, since it is not easy to obtain the ground truth on distance between vehicles, when both are moving. The images taken by one Galaxy Nexus (named as Galaxy Nexus 1 here) are treated as self-images. The focal ratio between phones is precomputed as described earlier in Section 2.2.D. The measured distance vs. ground truth is plotted in Fig. 2.18. It shows that OmniView can estimate the distance accurately, particularly up to 45 meters. Admittedly, these results, while very encouraging, are obtained when the vehicles are stationary. A part of our on-going work is to assess the accuracy of OmniView in real driving scenarios.
Figure 2.18: Distance estimated by OmniView compared with the ground truth.
C. Vehicular Communication
In OmniView, vehicles need to transmit detected vehicle images and map informa-
tion. As discussed in Section 2.2.C, the sizes of images will be in the range of 10∼14
KB. OmniView slices each image into 1-KB packets for transmission. Although we need to add other information, such as sequence number, into the Identify mes- sage, the size of these information is negligible compared with the image itself, so we just treat them as a little portion of the image in studying the communication performance. As to Map message, which only contains text, therefore we use 512 Bytes to convey the map.
We use simulation to study the performance of the vehicular communication of OmniView system. As a vehicular network, what is different from conventional networks is that every node (i.e. vehicle) in the network is moving. We need to simulate both the mobility of vehicles and the communication among vehicles. SUMO [29] (sumo-0.12.3) is used to generate the vehicle mobility, which is fed into NS2 [30] (NS2.34, which supports DSRC 802.11p) to carry out the network communication.
We simulated three transmission ranges: 60 m, 80 m and 100 m, we believe
proper maneuver to keep safe. We also studied the performance of OmniView under two different traffic conditions: one is dense mode, in which the traffic flow is relatively heavy and each vehicle has many neighboring vehicles; the other is sparse mode, in which every vehicle has fewer neighbors than in dense mode. The details of the parameters used in our simulation are listed in Table 2.4.
Table 2.4: Setting of Simulation for OmniView Vehicular Communication
Parameter Remark
Simulation Period 200 s
Number of Vehicles
400
(6 types of vehicles with different
length/speed/acceleration/deceleration; ve-
hicles’ speeds are dynamically changing in speed range.)
Speed 22∼36 m/s (50∼80 mph)
Traffic Density Sparse (58.8 vehicles/km)
Dense (102.7 vehicles/km)
Wireless Protocol DSRC 802.11p
Antenna Type OmniAntenna
Radio Propagation Model Two Ray Ground
Data Rate
6 Mbps (QPSK)
DSRC could support up to 27 Mbps data rate, but 6 Mbps is the optimal data rate which achieve good performance [21]
Message
Identify message (10∼14 KB, sliced into 1-KB
small packets)
Map message (512 B)
Message Life Time Image message: 0.6 s
Map message: 0.4 s
Transmission Method Periodic Broadcast
Transmission Frequency Identify message: 0∼3 images every 3.5±2.0 s
Map message: Once every 0.4±0.2 s
Transmission Range 60, 80, 100 meters
We measure the message reception rate, which is defined as:
#N odes in range&received the message
#N odes in sender0s transmission range (2.12)
The message reception rate for Identify and Map messages in two traffic modes are shown in Fig. 2.19. We can see that Map message could be exchanged very reli- ably for all the three transmission ranges in both sparse and dense traffic modes. In
case of Identify message, with short transmission distance, OmniView could achieve
about 78∼84% reception rate in dense mode, while in sparse mode, the number
goes up to 87∼90%. When the transmission range increases, the reception rate
decreases, especially in dense mode. The reason behind this is that every vehicle will be in the transmission ranges of more other vehicles, hence more network col- lisions will happen. But to a vehicle, the other vehicles which are far from it are less dangerous than the ones in short distance, so in many cases, we could stick to the shorter transmission range. Here we also set a relatively strict message life time for Identify message (i.e. 0.6s). We could allow longer life time for Identify message (because its main purpose is to get the corresponding TID), then we will have higher reception rate.
60 80 100 0 10 20 30 40 50 60 70 80 90
100 Map Message Reception Rate (Dense Mode)
Transmission Range (m) Reception Rate (%) 10 KB 12 KB 14 KB (a) 60 80 100 0 10 20 30 40 50 60 70 80 90
100 Identify Message Reception Rate (Dense Mode)
Transmission Range (m) Reception Rate (%) 10 KB 12 KB 14 KB (b) 60 80 100 0 10 20 30 40 50 60 70 80 90
100 Map Message Reception Rate (Sparse Mode)
Transmission Range (m) Reception Rate (%) 10 KB 12 KB 14 KB (c) 60 80 100 0 10 20 30 40 50 60 70 80 90
100 Identify Message Reception Rate (Sparse Mode)
Transmission Range (m)
Reception Rate (%)
10 KB 12 KB 14 KB
(d)
Figure 2.19: (a) Map message reception rate in Dense Traffic Mode (three types of image sizes and transmission ranges are compared; Map message is fixed to 512 Bytes). (b) Identify message reception rate in Dense Traffic Mode. (c) Map message reception rate in Sparse Traffic Mode. (d) Identify message reception rate in Sparse Traffic Mode.
As mentioned earlier, before the sender sends an Identify message, it calculates position for the detected vehicle, thereby the sender should be safe. For the re- ceiver, the probability of receiving a single Identify message might not be as high as Map message. But OmniView is a collaborative system, every vehicle will likely be detected by more than one vehicle moving behind it (in the same lane or different lanes). All those vehicles will send Identify messages to it, therefore the receiver will have more chances than what is shown in Fig. 2.19 to receive at least one of those Identify messages. Once it receives one and announces its TID (which is a Map message and its reception rate is high), the vehicles behind it could construct the VID-TID mapping. The subsequent Identify messages related to this receiver will turn into Map messages, which could be received more reliably. We can expect that in OmniView system, every vehicle will have high probability to obtain the positions of its neighboring vehicles.