Cover Image

Not for Sale

View/Hide Left Panel
Click for next page ( 58

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 57
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.

OCR for page 57
58 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.