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57
Invalid readings and null values are other commonly occur-
Cell phone Comment Cellular Steering ring obstacles. For example, in examining the rain gauge data,
detect PB antenna wheel angle
invalid (unrealistic) measurements were found, as shown in
Table 7.1 (196.088 mm). A rain gauge measurements algorithm
was developed to identify such unusual rainfall values and
Data Acquisition System Module
replace them with the average of the before-and-after values
in the case of the first problem. Additionally, Table 7.2 shows
another example of unrealistic data in which the GPS time
Inertial Driver face Front view Audio reports a null value for an entry. Algorithms were developed
sensors camera camera microphone to replace invalid or missing data, as demonstrated in the right
column of Table 7.2.
Details of challenges and potential problems regarding kine-
Figure 7.1. DAS used in Project 2 and Project 5. matic data, video data, reduced data, and other data sources are
discussed in the following sections.
shown in the left part of Figure 7.4. By filling the gaps, the real
routes can be accurately located, as shown in the right part of
Kinematic Data
Figure 7.4. Another example of data dropping out is illustrated
in Figure 7.5, which depicts a situation in which data (speed and Sensor measurements in each study to collect kinematic data
range) are missing for a few milliseconds while the valid target differ from one to another. Project 7 and Project 8 used a lane
is being tracked. To impute missing data, a linear interpolation tracker developed by VTTI (i.e., Road Scout) to detect the
may be performed. Figure 7.5 shows a speed profile with miss- vehicle's location within a lane. The maximum error was 6 in.
ing values for a target (upper part) and the same profile after for distance measuring and 1° for angular measuring. Simi-
performing a linear interpolation (lower part of the figure). lar equipment was used in Project 5 with a different accuracy
Figure 7.2. DAS used in Project 7.
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levels. In Project 2, radar units were operating at 77 GHz fre-
quency to track up to 15 targets. The sensing range was 100 m.
This range is reduced on a winding road because of the radar's
limited azimuth coverage. Project 5 included two forward-
looking radar units configured at 20 Hz, two side-looking radar
units configured at 50 Hz, and fields of view 120° wide. Radar
systems used in VTTI studies were operating at 20 Hz. Pro-
ject 6 had a radar range effective from 5 to 500 ft. Project 7 and
Project 8 used radar systems with effective ranges from 5 to
600 ft. Radar units used in Project 11 increased the effective
range to 700 ft.
When reviewing and reducing radar data, one must consider
the type of radar used, the rate of data collection, how "noisy"
the data are, and the assumptions used to mathematically
smooth the data. One typically derived variable was the TTC
between the subject vehicle and a leading vehicle. In Project 2,
enhanced TTC was computed incorporating the accelerations
of both vehicles, as well as the range to the vehicle ahead and the
speeds of the vehicles. In VTTI studies, the TTC was derived
from the range measured by either a radar-based VORAD for-
ward object detection unit or a TRW sensor. Acceleration and
deceleration of related vehicles were taken into consideration in
all studies except Project 11.
Radar switching targets is another difficulty. When radar
signal strength changes, the system might lose track of a tar-
Figure 7.3. Urban canyon blockage of GPS signals. get and then reacquire it. When this happens, the target will
be assigned a different ID number, which can cause confu-
sion. The manner in which the distance between the target
level. It used a monochrome charge-coupled device (CCD) and the equipped vehicle is recorded and organized in the
camera that observed painted lane markers or other nonpainted data set may also generate errors. The data collection system
visual features and could observe lane markers forward of the simultaneously tracks up to seven targets that change over
vehicle to approximately 30 m. time. As shown in Table 7.3, VTTI has developed algorithms
The radar systems used to detect surrounding objects and that identify and track the primary target of interest. To illus-
collect distance information for further derivation of data trate the importance of implementing such algorithms, con-
also vary from one project to another in numbers and config- sider the primary target of interest to be Target 231, as shown
urations. The variations in settings resulted in varied accuracy in Table 7.3. Before implementing the tracking algorithm
Figure 7.4. GPS gaps: (left) before links added; (right) after links added.
