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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Drivers’ detection of roadside targets when driving vehicles with three headlight systems during high beam activation</title>
      <link>https://trid.trb.org/View/1448519</link>
      <description><![CDATA[A previous open-road experiment indicated that curve-adaptive HID headlights driven with low beams improved drivers’ detection of low conspicuity targets compared with fixed halogen and fixed HID low beam systems. The current study used the same test environment and targets to assess whether drivers’ detection of targets was affected by the same three headlight systems when using high beams. Twenty drivers search and responded for 60 8 × 12 inch targets of high or low reflectance that were distributed evenly across straight and curved road sections as they drove at 30 mph on an unlit two-lane rural road. The results indicate that target detection performance was generally similar across the three systems. However, one interaction indicated that drivers saw low reflectance targets on straight road sections from further away when driving with the fixed halogen high beam condition compared with curve-adaptive HID high beam headlights and also indicated a possible benefit for the curve-adaptive HID high beams for high reflectance targets placed on the inside of curves. The results of this study conflict with the previous study of low beams, which showed a consistent benefit for the curve-adaptive HID low beams for targets placed on curves compared with fixed HID and fixed halogen low beam conditions. However, a comparison of mean detection distances from the two studies indicated uniformly longer mean target detection distances for participants driving with high beams and implicates the potential visibility benefits for systems that optimize proper high beam use.]]></description>
      <pubDate>Mon, 27 Feb 2017 09:27:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1448519</guid>
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    <item>
      <title>Analysis of Visual Adaptability in the Threshold Zone of Tunnels</title>
      <link>https://trid.trb.org/View/1369559</link>
      <description><![CDATA[Compare to the ordinary road sections, the traffic environment of tunnels is particularly different, especially when considering the visual environment. Due to the black hole effect, the threshold zone is regarded as accident-prone location. But in the engineering practice in China, the greatest change of the illumination in tunnels happened not at the tunnel portal but at somewhere inside the tunnel. This paper aims at discovering the distance range with the highest driving risk in threshold zone of tunnel. A field experiment has been conducted in a typical tunnel in Hunan Province, China. Based on the experimental data analysis, the relationship is built between the target detection distance, the distance from the tunnel portal, and the ratio of illumination, respectively. The results showed that at operating speed of 60 km/h, the distance range with the highest driving risk is 70 m to 109 m from the tunnel portal. The distance range is 50 m to 87 m from the tunnel portal for 80 km/h and 42 m to 79 m from the tunnel portal for 100 km/h. Based on target detection distance, the illumination design for tunnel lighting can be optimized.]]></description>
      <pubDate>Fri, 23 Oct 2015 09:45:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1369559</guid>
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    <item>
      <title>Multiple target detection and tracking by interacting joint probabilistic data association filter and Bayesian networks: Application to real data</title>
      <link>https://trid.trb.org/View/1365587</link>
      <description><![CDATA[This paper proposes an algorithm of multiple target detection and tracking on road, developed for the laser scanner data. It is based on Bayesian networks for calculating the detection probability of target used in a joint probabilistic data association (JPDA) filter. The authors propose a method based on the integration of detection probability of target in the JPDA filter, in which the joint probabilities of associations are calculated for multiple targets. It then solves the problem of founding one or more observation in more than one gate of target by constructing hypothesis. A Bayesian network is used to determine this probability of detection. It includes a target model and takes into account the contextual information such as the size of the target, the distance between targets and the center of the sensor characteristics (position, measurement uncertainty). The method is applied to real data provided by a scanning laser sensor with multiple targets. as well as the possibility that an object enters and leaves the scene.]]></description>
      <pubDate>Wed, 26 Aug 2015 10:54:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/1365587</guid>
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    <item>
      <title>Drive Aware Task: Measuring Target Detection in a Visual Clutter in the Driving Context</title>
      <link>https://trid.trb.org/View/1338953</link>
      <description><![CDATA[Age-related declines in the ability to select targets in a visual clutter across an extended visual field has been identified as a major cause of higher crash risks among older drivers. An effective measure of target detection in a cluttered driving scene could be useful to inform about an older driver’s decline in attentional abilities and the potential influence on driving safety. This paper describes the development and initial examination of a task, the Drive Aware Task, designed to measure attentional abilities in the context of driving. The authors conducted two experiments to investigate the effectiveness of the task. Their results showed that older drivers’ performance on the Drive Aware Task was associated with performance on a cognitive task measuring attentional visual field, suggesting that the Drive Aware Task is a valid measure of attentional abilities among older drivers. Performance on the task also revealed significant age differences. Compared to younger drivers, older drivers in general performed worse on the Drive Aware Task; however, the size of age differences varied among different task conditions (travelling left, right or straight). Moreover, older drivers’ performance on the Drive Aware Task was consistent with their performance on simulated driving. This evidence demonstrates the effectiveness of the Drive Aware Task. This task can potentially be a useful measure of attentional abilities in the context of driving, such as an assessment tool for cognitive fitness-to-drive among older drivers.]]></description>
      <pubDate>Fri, 20 Mar 2015 15:21:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1338953</guid>
    </item>
    <item>
      <title>The Effects of Voice Technology on Test Track Driving Performance: Implications for Driver Distraction</title>
      <link>https://trid.trb.org/View/1142055</link>
      <description><![CDATA[The objectives of this research were: (1) to compare the distraction potential of voice-based versus visual/manual interfaces for selected transactions undertaken while driving, (2) to examine the effect of performing tasks of differing complexity on driving performance, and (3) to evaluate the potential of using eye-tracking technology to make inferences about changes in subjects’ allocation of attention while performing specified in-vehicle tasks while driving. Twenty-one subjects completed two sets of eight laps around a 7.5-mile test track during two four-hour sessions. They drove an instrumented vehicle while performing a combination of car following, peripheral target detection, and secondary (in-vehicle) tasks of varying complexity. Subjects performed one set of laps with each of two interfaces, voice-based and visual/manual. Secondary tasks comprised three categories including baseline tasks (radio tuning, phone dialing), simple tasks (message retrieval plus voice memo creation), and complex tasks (simple task components plus phone dialing and information retrieval from automated phone systems). Measures of driving performance, target detection, secondary task performance and eye movements were recorded. Analyses were conducted to determine whether the voice-based interface reduced the relative distraction potential for secondary tasks of varying complexity. Performing secondary tasks while driving resulted in significant decrements to vehicle control, target detection and car-following performance. The voice-based interface helped reduce the distracting effects of secondary tas k performance. Improvements were relatively minor and limited to vehicle control and visual performance measures. There was no effect on car-following measures, suggesting the voice interface had little effect on cognitive distraction. The results suggest that voice interfaces may not provide enough help to overcome the cognitive distraction associated with secondary tasks of increasing complexity, particularly in driving situations that require time-space judgments and tactical decision-making.]]></description>
      <pubDate>Fri, 29 Jun 2012 16:11:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1142055</guid>
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    <item>
      <title>Distraction Effects of Manual Number and Text Entry While Driving</title>
      <link>https://trid.trb.org/View/1136533</link>
      <description><![CDATA[An experiment was conducted to assess the distraction potential of secondary tasks performed using in-vehicle systems (radio tuning, destination entry) and portable phones (10-digit dialing, selecting contacts, text messaging) while driving. One hundred participants, ages 25-64, completed a single session in which they drove a low-fidelity (PC-based) simulator while performing the secondary tasks. The phone tasks were performed with two smart phones, one with a touch screen interface (iPhone) and one with a hard button interface (Blackberry). The Dynamic Following and Detection (DFD) driving protocol, which combines car-following with target detection, in which drivers responded to simple visual targets presented in the simulated roadway display, was used. Each combination of primary (driving) and secondary task was performed during a single 3-minute drive. Driving performance metrics included: lane position variability, car-following delay, target-detection accuracy and target-detection response time. It was found that text messaging was associated with the highest level of distraction potential. Ten-digit dialing was the second most distracting task; radio tuning had the lowest level. Although destination entry was no more demanding than radio tuning when task duration effects were eliminated with DFD metrics, it exposes drivers to more risk than radio tuning and phone tasks due to its considerably longer duration. Modest differences between phones were observed, including higher levels of driving performance degradation associated with the touch screen relative to the hard button phone for several measures. Additional analyses demonstrated that the way in which task duration is considered in the definition of metrics influenced the outcomes of statistical tests using the metrics. The results are discussed in the context of the development of guidelines for assessment of the distraction potential of tasks performed with in-vehicle information systems and portable devices. Additional analyses were conducted to compare the DFD and Alliance and decision criteria in a simulated compliance scenario. With the large sample size (N = 100), both protocols supported the conclusion that neither text messaging nor 10-digit dialing is suitable for combining with driving; however, when a smaller (N = 40) sample was used, the protocols led to different conclusions. Additional analyses found that for, using just the vehicle performance metrics (not the eye glance metrics), samples of 20 participants did not provide sufficient statistical power to differentiate among secondary tasks. Driver age had significant effects on both primary and secondary task performance; younger drivers completed more secondary task trials on a given drive, with relatively less primary task interference than older drivers. Tests conducted using samples with wide age ranges (25-64) required larger samples to compensate for reduced homogeneity relative to samples with narrow age ranges. Half of the participants were given specific monetary incentives, while half received an equivalent amount in an unspecified completion bonus. Incentives had some effects, primarily among older participants, but no consistent overall effects on primary or secondary task performance, or on the emphasis given by drivers to the primary task.]]></description>
      <pubDate>Fri, 13 Apr 2012 15:54:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1136533</guid>
    </item>
    <item>
      <title>Developing a Test to Measure Distraction Potential of In-Vehicle Information System Tasks
in Production Vehicles</title>
      <link>https://trid.trb.org/View/1136519</link>
      <description><![CDATA[Three experiments were conducted to assess a test of distraction potential for in-vehicle information systems (IVIS) and portable devices used while driving. The test used a low-fidelity (PC-based) driving simulator; sensors record drivers’ control inputs in stationary production vehicles. Participants performed car-following and target detection together with secondary tasks. Experiment 1 examined the effects of two levels of driving task (car-following) difficulty and two detection tasks on test sensitivity. Detection tasks included a head-mounted task (HDT) and a computer-generated multiple-target task (MDT), which incorporated simple targets into the simulated roadway display. Secondary tasks included simple (Circles) and complex (navigation destination entry) visual-manual tasks and a hands-free auditory-vocal task (N-back). The MDT was more sensitive to task load differences, while the HDT created problems for the eye tracker. Increasing car-following task difficulty had no effect on metric sensitivity. The complex visual-manual task was more disruptive than the simple visual-manual task or the auditory-vocal task. The second experiment compared metrics provided by two occlusion paradigms: (1) traditional occlusion, which involves intermittent masking of the task to simulate the visual demands of driving, and (2) Enhanced Occlusion Task (EOT), which added auditory tracking to more realistically simulate driving task demands. Task duration estimates with EOT were closer to static completion times than those obtained with traditional occlusion; however their usefulness in estimating task duration requires a stronger connection to comparable estimates obtained from a driving protocol. EOT did not improve the R (task resumability) metric, although there was no independent evidence to support the expectation of differences in this metric. The R metric was not related to driving performance degradation. Auditory tracking performance metrics obtained in the EOT paradigm revealed performance degradation consistent with effects observed with simulator metrics. Experiment 3 used the simulator-based test to assess the distraction potential of navigation tasks performed with three systems with comparable functionality, including one original equipment manufacturer (OEM) and two portable systems, which had been independently rated as having different levels of usability. Metrics revealed strong and consistent differences between driving alone and driving with a secondary task. Three metrics (car-following coherence, detection task response time, and proportion of long glances) revealed differences between the (simple and complex) navigation tasks across all systems. Two metrics (standard deviation of lane position [SDLP] and detection task proportion correct) exhibited significant Systems x Task interactions; differences between navigation tasks were not consistent across systems. It was concluded that developing a simulator-based distraction potential test is feasible. Core metrics include those sensitive to visual-manual task conditions (SDLP, car-following delay, and detection task response time) and those sensitive to auditory-vocal task conditions (car-following delay, detection task response time, and detection task proportion of correct responses). Measures based on eye position data, primarily the proportion of long glances away from the forward roadway, revealed significant promise. Estimates of distraction potential can be combined with task duration estimates provided by the EOT to compute estimates of drivers’ exposure to risk.]]></description>
      <pubDate>Thu, 12 Apr 2012 16:24:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/1136519</guid>
    </item>
    <item>
      <title>Effects of the Viewing Context on Target Detection. Implications for Road Lighting Design</title>
      <link>https://trid.trb.org/View/920335</link>
      <description><![CDATA[The Small Target Visibility (STV) model is the main model used to assess the quality of road lighting installations (IESNA, 2000). However, this model is based on a simple detection task in foveal vision using psychophysical data from laboratory conditions. The purpose of this study was to evaluate the impact of a complex background and apparent motion on target detection performance in mesopic vision, for 3 luminance contrasts, with reference to the STV scenario. To do so, subjects were invited to detect standard square targets varying in terms of contrast presented in 3 Conditions: a uniform background, still images, and a video. Luminance levels were chosen in the mesopic domain relevant for road lighting at night. Images and video were chosen in relation to a driving task at night. Results showed that both the spatial context and apparent motion had a negative impact on peripheral target detection performance: contrasts which are easy to detect in conditions close to the STV reference data may lead to poor performance if one adds context variables. These results give evidence that the STV model used for road lighting design based on laboratory data is limited, which strengthens previous results (Mayeur et al., 2008). The results are discussed in relation to the field factor used by practitioners to compensate for the differences between the STV reference scenario (detection of a small square target on a lit road while driving) and the STV psychophysical reference data.]]></description>
      <pubDate>Mon, 28 Jun 2010 07:43:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/920335</guid>
    </item>
    <item>
      <title>The effect of the driving activity on target detection as a function of the visibility level: Implications for road lighting</title>
      <link>https://trid.trb.org/View/914411</link>
      <description><![CDATA[The small target visibility model used in road lighting design is based on a strong simplification of the driving task. To specify the driver's visibility needs in a way consistent with state-of-the-art lighting engineering practice, a field experiment was designed in order to investigate target detection performances, comparing driver and passenger status conditions. Sixteen target visibility levels (VL) were used. In the driver status, 34 participants had to press a button as soon as they detected the target stimulus placed on the experimental lighted section. In the passenger status, the same participants had to detect the target stimulus while the experimenter drove. The results show that the passengers' performances (detection distance) were higher than the drivers' performances (p = .0014). Furthermore, the higher the VL, the higher the detection distance (p < .0001). These results lead up to modify the reference scenario in order to take into account human factor components for road lighting design.]]></description>
      <pubDate>Wed, 31 Mar 2010 07:45:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/914411</guid>
    </item>
    <item>
      <title>Comparison of Maximum Distance Metrics for Use in the Remote Sensing of Small Targets</title>
      <link>https://trid.trb.org/View/755942</link>
      <description><![CDATA[There are many applications for small target detection in engineering: topographic mapping, infrastructure inventories, and pre-engineering design. Near shore marine applications include: mapping breakwaters, piers, navigation structures, pilings, and vessel traffic patterns. The application driving this research is the development of a surveillance system for the Canadian Coast Guard. As a result, a new and innovative application of small target detection techniques is being developed at the Department of Geodesy and Geomatics Engineering, UNB, Canada. This work is being done in support of the development of a strategic decision making tool to be used to predict where in Canadian waters marine incidents are most likely to occur. Previous research in the use of hyperspectral imaging for search and rescue, resulted in the development of fast, nonparametric "spatio-spectral" template subpixel object detection algorithm. The results of this work are being adapted and enhanced for use with the new, commercially available spaceborne high-resolution optical imagery. Research is being performed on the employment of a weighted Euclidean distance metric to enhance the "spatio-spectral" template by exploiting the variance/covariance information surrounding a potential target. The detection results using the new weighted Euclidean distance metric were excellent. The best results were had using a 3×3 kernel with all 16 targets being detected.]]></description>
      <pubDate>Mon, 16 May 2005 15:25:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/755942</guid>
    </item>
    <item>
      <title>MOVING TARGET DETECTOR/AIRPORT SURVEILLANCE RADAR (ASR-7) FIELD EVALUATION</title>
      <link>https://trid.trb.org/View/171396</link>
      <description><![CDATA[The Moving Target Detector (MTD) II, a sophisticated radar processor, was evaluated to determine its capability to provide improved radar detection in an air traffic control (ATC) environment. The MTD II was installed on one channel of an airport surveillance radar (ASR-7) at Burlington, Vermont. The major objective of testing was to compare the performance of the MTD II with that of the ASR-7 Moving Target Indicator (MTI). This report concentrates on the comparative probability of detection, false alarm rate, MTI improvement factor, subclutter visibility, dynamic range, velocity response, and the simultaneous flight test results of the two systems. Comparison of the MTD II to the MTD I system is made when necessary to show major improvements or deficiencies in the MTD II design. The results of the tests have shown that the MTD II provides surveillance capabilities superior to those of the ASR-7/MTI. (Author)]]></description>
      <pubDate>Wed, 29 Jan 2003 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/171396</guid>
    </item>
    <item>
      <title>AI TRACKER FOR SAR RADAR DETECTION FIELD TRIAL</title>
      <link>https://trid.trb.org/View/731687</link>
      <description><![CDATA[In June 1997, Oceans Ltd. began the Transportation Development Centre (TDC) sponsored this field project with the goal of collecting high-quality, digital radar data capable of supporting future research into the enhancement of digital radars for small target detection. Major barriers to the development of digital radar processing technologies have been the difficulties and expense of field-testing developmental systems. The recent development of the modular radar interface (MRI) by Sigma Engineering of St. John's has minimized future requirements for field programs by providing a means of making high-resolution digital recordings of unprocessed radar images on 5 mm DATs. The recorded video can be used over and over in a lab setting for the testing of digital detection algorithms. The field experiment successfully demonstrated a methodology for the collection of high-quality, digital radar data using the Sigma MRI. The data set collected under the project met all project objectives and is currently being used for developmental testing of the Raytheon AI Tracker. Radar video collected under this contract will be a key component in the development of the AI Tracker and future radar processing technologies for small target detection.]]></description>
      <pubDate>Tue, 14 Jan 2003 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/731687</guid>
    </item>
    <item>
      <title>MOVING TARGET DETECTOR DATA UTILIZATION INVESTIGATION</title>
      <link>https://trid.trb.org/View/155856</link>
      <description><![CDATA[The Moving Target Detector (MTD), developed by M.I.T.'s Lincoln Lab., the Massachusetts Institute of Technology (MIT) Lincoln Laboratory under contract to the Federal Aviation Administration (FAA), M.I.T.'s Lincoln Lab., was designed as an improved video processor for use with the Airport Surveillance Radars within the Automated Radar Terminal System (ARTS) III automated Air Traffic Control (ATC) system. This device interfaces between the analog radar system and the digital automatic tracking system to provide automatic target detection and false alarm regulation. In addition to range, bearing, and amplitude data, the MTD provides doppler information on detected targets. This data is in the form of unconsolidated reports that are to be centroided and tracked to provide a primary radar data base for ATC functions. In order to evaluate the potential benefits of this MTD data on the ATC tracking operation, the present study was conducted using experimental data gathered by the FAA and Lincoln Laboratory with an FPS-18 radar at the National Aviation Facilities Engineering Center during the summer of 1975. The principal investigations focus on: development of a centroid algorithm for extracting significant target features; and using these features within the subsequent tracking function.]]></description>
      <pubDate>Sun, 27 Oct 2002 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/155856</guid>
    </item>
    <item>
      <title>EVALUATION OF THE PRODUCTION IMPROVED RADAR QUANTIZER GROUP MODIFICATION TO THE PRODUCTION COMMON DIGITIZER</title>
      <link>https://trid.trb.org/View/41511</link>
      <description><![CDATA[An improved radar quantizer group (RQG) was installed in the production common digitizer (PCD) located at the Elwood, New Jersey, National Aviation Facilities Experimental Center long range radar site. The modified RQG improves search target detection in radar clutter environments. A series of subsystem and system tests was conducted to compile data comparing digitizer performance using the improved RQG/PCD with performance from an unmodified PCD. Test results indicate increased search target detection and reduced automatic clutter eliminator (ACE) total blank in weather clutter areas. It was concluded that the modified PCD increased search track life in radar clutter environments. (Author)]]></description>
      <pubDate>Sat, 13 Jul 2002 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/41511</guid>
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    <item>
      <title>ANALYSIS OF VISUAL DETECTION PERFORMANCE (FALL 1978 EXPERIMENT)</title>
      <link>https://trid.trb.org/View/82910</link>
      <description><![CDATA[From 11 September 1978 to 6 October 1978 a visual detection experiment was conducted in Block Island sound by the U.S. Coast Guard Research and Development Center. It was the first in a series of experiments designed to improve search planning guidance contained in the National Search and Rescue Manual. This was a controlled experiment involving 82 and 95 foot cutters, 41 and 44 foot boats, helicopters, and fixed wing aircraft searching for white 16 foot boat targets anchored at predetermined locations within the search area. Through the use of a microwave ranging system, the positions of searchers and targets could be accurately reconstructed to determine the lateral range of targets that were detected, as well as targets not detected. Thus, probability of detection versus lateral range curves could be developed, and, by integrating these curves, sweep width could be determined as well. A total of 695 detection opportunities were generated. A sophisticated binary multivariate logistic regression computer program was used to develop sweep width estimates for the environmental conditions experienced. Of the eight visual detection parameters investigated, visibility, wind speed, swell height, cloud cover, search unit type, and duration of search were determined to have a significant effect on sweep width. The sweep width is an excellent measure of search unit performance. A more rapid degradation of sweep width was found for deteriorating environmental conditions than is now predicted by the National Search and Rescue Manual. The methods used to conduct this experiment and analyze the data were found to be successful, and are recommended for future experiments.]]></description>
      <pubDate>Sun, 30 Jun 2002 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/82910</guid>
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