<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Effects of driver direct visibility in passenger vehicles on the risk of turning crashes with pedestrians</title>
      <link>https://trid.trb.org/View/2709661</link>
      <description><![CDATA[This study examined if characteristics of direct visibility metrics in passenger vehicles, such as large blind zones, corresponded with higher risk of turning crashes with pedestrians aged 16 years and older. It also evaluated effects of vehicle front structures on some direct visibility metrics.  Single-passenger vehicle, single-pedestrian crashes in seven states were included. Direct visibility metrics included driver- and passenger-side blind zone sizes, front nearest visible point (NVP) distance, and front field of view (FOV) width; measurements were taken on 168 combinations of vehicle make, series, and redesign years. Logistic regression analyses evaluated effects of visibility metrics and other factors on the odds of a left- or right-turning pedestrian crash, relative to straight-moving. Linear regression analyses examined effects of front structures on blind zone sizes and front NVP distances.  For left-turning pedestrian crashes, large (> 30%) and medium (> 20% and ≤ 30%) driver-side blind zones were, respectively, associated with a significant 75.3% and 62.4% increase in the odds, compared with a small size (≤ 20%). A narrow front FOV (≤ 85°) was associated with a significant 49.6% increase in the odds, compared with a wide FOV (> 90°). A long front NVP distance (> 9 m) was associated with a significant 35.3% increase in the odds, compared with a short distance (≤ 6 m). No significant associations were found between visibility metrics and right-turning pedestrian crashes.  Larger driver-side blind zones, longer front NVP distances, and narrower front FOVs increased risk of left-turning pedestrian crashes. Effects of A-pillars, side mirrors, hoods, and windshields on blind zone sizes and front NVP distances were also validated. Findings could help automakers improve safety for road users outside vehicles with changes in vehicle design to enhance drivers’ direct vision and improvements to AEB technology to address vehicle-turning conflicts.]]></description>
      <pubDate>Thu, 18 Jun 2026 16:34:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709661</guid>
    </item>
    <item>
      <title>Real-time identification of cooperative perception necessity in road traffic scenarios</title>
      <link>https://trid.trb.org/View/2667137</link>
      <description><![CDATA[Cooperative perception (CP) has shown great potential in enhancing traffic safety with Vehicle-to-Everything (V2X) communications. However, its substantial communication burden makes resource-efficient CP crucial, especially when a single vehicle’s intelligence with adequate perception is sufficient to handle most traffic scenarios. Therefore, to reduce the resource consumption of CP, it is essential to identify the traffic conditions under which CP should be applied. This paper addresses this issue by identifying the necessity of CP among road users, and evaluating whether their sensory information is adequate for ensuring traffic safety. We propose a practical framework to assess CP necessity by leveraging bird’s-eye view data from roadside cameras. The framework begins with video-based object localization and tracking to identify the position and movement of each road user. Next, we use a stochastic motion prediction model to analyze the collision risks between pairs of road users. Simultaneously, pairwise perception analysis is used to assess the probability of one road user perceiving another, determining if a road user falls within a blind spot. Road users with both collision risk and potential perception blind spots are identified as requiring CP. Field tests are conducted using real-world scenarios at two complex intersections in Madison, WI, which include a diverse range of road users, such as various vehicles and vulnerable pedestrians and cyclists. The results demonstrate that the proposed framework can effectively identify the safety-challenging scenarios that require CP in complex traffic environments. With only 0.1% of situations in our field test requiring CP, implementing the proposed framework can save a significant amount of communication bandwidth and computational costs while ensuring the same level of safety. Our code and data will be made available upon the acceptance of this paper.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667137</guid>
    </item>
    <item>
      <title>Boston Blind Zone Safety Initiative: Current Fleet Analysis, Market Scan, and Proposed Direct Vision Rating Framework</title>
      <link>https://trid.trb.org/View/2692319</link>
      <description><![CDATA[In about one-quarter of low-speed, truck-involved, vulnerable road user fatalities in the U.S., a driver’s direct vision was impaired. A driver has direct vision of an object outside the vehicle when it can be seen without the aid of mirrors or camera displays. Vehicles vary in how near drivers can see outside the vehicle to the front and to the side. This paper reports a first-in-the-U.S. effort with the U.S. Department of Transportation Volpe Center, the Boston Public Health Commission, and the Boston Transportation Department to assess the direct vision for vehicles used for Boston’s Schools, Fire, and Public Works Departments. The research team quantitatively measured direct vision in 21 vehicles using both a manual approach and a camera-based approach. Using these methods, this paper proposes a direct vision rating system that the City of Boston and other fleets can incorporate into procurement. The proposed system includes front and passenger five-star ratings based on the distance at which a child or adult would be visible directly in front of or to the passenger side of the vehicle, calibrated to federally and locally defined intersection geometric standards. A five-star vehicle enables drivers to see children in the crosswalk and children on bicycles in a buffered bicycle lane. In 11 of the 21 vehicles, drivers whose vehicle was stopped at the stop bar before a crosswalk at an intersection could not adequately see a child in the crosswalk in front or an adult on a bicycle on the side.]]></description>
      <pubDate>Wed, 15 Apr 2026 10:36:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692319</guid>
    </item>
    <item>
      <title>MTRCP: Multimodal Two-Level Fusion Architecture for Roadside Cooperative Perception</title>
      <link>https://trid.trb.org/View/2625411</link>
      <description><![CDATA[With the rapid development of self-driving technologies, autonomous driving still faces challenges because of the complexity of traffic environments, making accurate and stable environmental perception crucial. Roadside units (RSUs) can significantly enhance the perception range of autonomous vehicles, effectively addressing blind spots and improving traffic safety. This article proposes the Multimodal Two-Level Fusion Architecture for Roadside Cooperative Perception, a novel framework that leverages RSUs to enable comprehensive and over-the-horizon perception capabilities. We fuse point clouds and images at the first level to generate local detection results and then perform a second-level fusion to integrate these local results, producing full-area perception. Experimental validation with the DAIR-V2X-seq dataset and data collected at the China Telecom Smart Grid Test Park demonstrates the effectiveness and feasibility of the proposed cooperative perception architecture.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:23:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625411</guid>
    </item>
    <item>
      <title>Assessment of the stopping for right-turning large vehicles policy in Nanjing: Effectiveness and determinants</title>
      <link>https://trid.trb.org/View/2604565</link>
      <description><![CDATA[This study evaluates the effectiveness of Stopping for Right-Turning Large Vehicles Policy in Nanjing, designed to mitigate accidents attributed to blind spots and delayed braking of large trucks at intersections. Using high-resolution conflict data from four signalized intersections in Jiangning District, collected via unmanned aerial vehicles (UAVs) and roadside video, the research employs K-means clustering for conflict severity classification and binomial Logit regression to identify critical determinants. Results reveal the policy exhibited limited statistical significance in reducing severe conflicts (p > 0.05). Regression analysis quantified four critical determinants: absence of motorized/non-motorized segregation (OR=1.82, + 81.6% severity odds), elevated stop-line speeds (OR=1.32, + 31.9%), failure to yield (OR=2.45, + 145%), and crossing the street within the zebra crossing (OR=0.19, -81.0%). The analysis demonstrates that infrastructural deficiencies and behavioral non-compliance outweigh the policy's standalone impact. Based on these findings, the study proposes a holistic optimization framework integrating physical separation measures, enhanced signage, dynamic traffic signal adjustments, and data-driven enforcement strategies. Methodologically, this study innovatively combines unsupervised learning for conflict categorization, providing a scalable framework for evaluating urban traffic policies. This research underscores the necessity of multi-dimensional interventions-spanning infrastructure, enforcement, and public education-to achieve sustainable improvements in intersection safety. The findings offer actionable insights for policymakers to refine regulatory measures and enhance road safety in rapidly urbanizing environments.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604565</guid>
    </item>
    <item>
      <title>Vehicle blind zones and pedestrian safety at intersections: Identifying high-risk scenarios using mathematical simulations</title>
      <link>https://trid.trb.org/View/2617070</link>
      <description><![CDATA[This study presents a novel simulation-based methodology to systematically assess how vehicle blind zones, intersection geometry, and the speed and direction of both vehicles and pedestrians interact to influence pedestrian visibility at intersections. Focusing on a diverse set of vehicle designs and intersection scenarios, the framework identifies high-risk situations where blind zones may critically impair a driver’s ability to detect and respond to crossing pedestrians. Blind zone data from 20 vehicles representing a range of classes were used. A custom simulation code modeled interactions between crossing pedestrians and vehicle blind zones in four intersection geometries: baseline, narrow lane, wide lane, and offset crosswalk. For each geometry, vehicle speed (4.2 m/s or 15 km/h, 5.6 m/s or 20 km/h, 6.9 m/s or 25 km/h), vehicle trajectory (straight, left, right), and turning trajectory (sharp, average, wide) were simulated. Crossing pedestrians were modeled for two travel directions (from the driver-side and passenger-side A-pillar) at three speeds (0.8, 1.2, 1.8 m/s). Output metrics included time the pedestrian spends in the blind zone, percentage of crossing time in the blind zone, and available driver reaction time once the pedestrian is visible to the driver. Vehicle blind zone areas ranged from 242 to 478 m2. Pedestrians were predominantly obscured during left-turn maneuvers, especially when approaching from the driver-side A-pillar, resulting in an average time in the blind zone of 1.7 s compared with 0.8 s for passenger-side approaches. Lower vehicle speeds combined with fast pedestrian speeds further increased this duration, while higher speeds reduced the available driver reaction time. Intersection geometry and turning radius also affected these metrics. Regression analyses indicated that although blind zone area did not predict total obscuration time, it was significantly associated with delayed pedestrian entry/exit and longer available driver reaction time. This study used a novel simulation methodology to demonstrate that vehicle blind zones can critically affect pedestrian visibility, particularly in left-turn scenarios involving driver-side pedestrian approaches. While blind zone area is an important metric, finer design features (e.g., A-pillar dimensions) and dynamic factors such as vehicle speed, turning behavior, and intersection layout play a crucial role in determining pedestrian visibility.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617070</guid>
    </item>
    <item>
      <title>Assessing advanced driver assistance systems in police vehicles under demanding conditions</title>
      <link>https://trid.trb.org/View/2594127</link>
      <description><![CDATA[Advanced driver-assistance systems (ADAS) are technologies that can enhance drivers’ safety by relieving them from some driving related activities. However, police driving conditions and demands are different from those of civilian drivers. The objective of this study was to assess the impact of ADAS such as forward collision warning (FCW), automatic emergency braking (AEB), and blind spot monitoring (BSM) on police officers’ driving performance, workload, and trust in vehicle safety to provide personalised solutions for police vehicles. A driving simulation study was conducted with 18 police officers. ADAS use was assessed under various driving conditions and while officers were engaged in non-driving related tasks. Findings suggested that the FCW and AEB systems improved officers’ driving performance, while the BSM system had limited effectiveness due to low salience. ADAS were beneficial under normal driving conditions and when officers were using in-vehicle technology; however, they did not help officers in pursuit conditions.]]></description>
      <pubDate>Tue, 18 Nov 2025 11:04:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594127</guid>
    </item>
    <item>
      <title>Using Head-Mounted Virtual Reality to Measure Dynamic Driver Sight Distances and Blind Spots</title>
      <link>https://trid.trb.org/View/2625595</link>
      <description><![CDATA[In highway and road design, accurately measuring a driver’s line of sight is critical to ensuring unobstructed views that allow drivers to detect, respond to, and safely stop their vehicles before colliding with an object or pedestrian. This safe stopping distance, referred to as Stopping Sight Distance (SSD), is a key design control variable, especially when determining appropriate vehicle speeds on roadway segments with curves, grades, or intersections.  At intersections, sight distance becomes even more vital due to frequent interactions between vehicles, pedestrians, and bicyclists. These interactions, occurring across multiple directions—straight, right turns, and left turns—significantly increase the potential for conflicts, esp. the conflicts between vehicles and pedestrians or bicyclists. Traditional methods for measuring sight distance are, however, time-consuming, labor-intensive, and prone to variability based on the engineer’s experience.  A driver’s blind spots, or blind zones—areas outside their field of view—can further complicate sight distance measurement. These blind spots are influenced by factors such as the driver’s eye height and the vehicle’s design. For example, large A-pillars and oversized rear mirrors in taller vehicles can obstruct a driver’s view, particularly during turning maneuvers. Properly evaluating both sight distance and blind zones is crucial for selecting design speeds and optimizing roadway features to improve safety and functionality.  Virtual Reality (VR) and Augmented Reality (AR) technologies offer promising solutions for addressing these challenges and improving the state-of-the-art technology for measuring and understanding drivers’ SSDs and blind spots. By combining realistic driving simulations with real-time eye and head tracking, VR/AR can enable efficient and accurate assessments of sight distance and driver blind zones more efficiently and more comprehensively than is currently done. This project seeks to explore the following research questions:  1. How does dynamic driver modeling differ from static driver modeling regarding the driver’s ability to observe the road, pedestrians, and their surrounding environment?  2. How does dynamic driver behavior affect sight distance and driver blind zones at intersections?  3. How will the choice of driving speed and path differ with and without driver blind spots and sight limitations?  Answering these questions will provide a deeper understanding of driver blind spots and sight limitations, enabling engineers to optimize roadway designs for enhanced safety and efficiency. Furthermore, these insights can assist drivers in selecting appropriate speeds and navigation paths, reducing the likelihood of conflicts with pedestrians and bicyclists. By leveraging VR/AR technologies, this research aims to streamline the evaluation process and advance roadway design practices, ultimately contributing to safer, more efficient transportation systems.]]></description>
      <pubDate>Mon, 17 Nov 2025 14:56:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2625595</guid>
    </item>
    <item>
      <title>The Effect of Front Pillar Blind Spots on Visual Behavior at Intersections in an Immersive Driving Simulator</title>
      <link>https://trid.trb.org/View/2582070</link>
      <description><![CDATA[This study focuses on drivers' visual behavior. It experimentally investigates the effect of the blind spots around the front pillars on driving at intersections using an immersive driving simulator. As a result, it was confirmed that visual behaviors, such as the driver's head-turning, increased with the enlargement of the blind spots. The effect was evaluated using a visibility score based on these visual behaviors.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582070</guid>
    </item>
    <item>
      <title>Are directional or non-directional hazard warnings more helpful for drivers with homonymous visual field loss?</title>
      <link>https://trid.trb.org/View/2587418</link>
      <description><![CDATA[People with homonymous visual field loss (HVFL), the loss of vision in the same half of the visual field in both eyes, are permitted to drive in some jurisdictions. However, the HVFL may cause delayed responses to hazards from the side of their vision loss (blind side). Warnings that indicate hazard direction may be beneficial. This study compared the effects of directional and nondirectional vibrotactile warnings on gaze behaviors and driving responses to pedestrian hazards in a driving simulator. Directional warnings consisted of vibrations on either the left or right side of the seat cushion to indicate the direction of an approaching hazard, while nondirectional warnings provided vibrations on both sides. Warnings were triggered at a 3-s time-to-collision, allowing HVFL drivers time to react. Both warning types were equally efficacious in reducing blind side collisions, with no difference in hazard brake response times. However, differences in gaze fixation behaviors suggest that participants with HVFL used the directional information, as they self-reported, to guide their gaze toward hazards on the blind side. Directional warnings were strongly preferred and resulted in quicker fixations on hazards.]]></description>
      <pubDate>Mon, 22 Sep 2025 08:48:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587418</guid>
    </item>
    <item>
      <title>Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout</title>
      <link>https://trid.trb.org/View/2549286</link>
      <description><![CDATA[While driving, maintaining a sufficient distance helps reduce collision risk. A time gap of two or three seconds on urban roads from a vehicle ahead is advised in defensive driving. The scenario becomes even more challenging in densely populated and developing countries because of limited road infrastructure, lane indiscipline, and heterogeneous traffic. The safe distance between vehicles and the driver’s reaction can be used as surrogate safety measures (SSMs) to evaluate defensive driving behaviour. This paper presents a case study evaluating defensive driving behaviour using the vision-based methodology and UAV video. This paper proposes two novel SSMs based on distance and acceleration and studies defensive driving behaviour, such as “for how long did a vehicle keep driving under another vehicle’s blind spots?” and “how is a vehicle driving (an interaction pattern) when another vehicle ahead is in its stopping distance range?.” Finally, each driver’s star rating depends on their interactions with other vehicles. The authors observed that around 48 % of the vehicles did not follow defensive driving practices. In their vehicle inter-class interaction analyses, the authors also found 16.6 % Rear-End, 6.3 % Side-Swipe, and 1.5 % Angled collision risks occurred between car-car, car-car, and 2Wheeler(2W)-car, respectively. The authors' methodology could help traffic law enforcement agencies and policy-makers elevate road traffic safety by taking counter-measures against the low-star vehicle categories in developing countries. Example videos of star rating are available on https://www.youtube.com/@YagnikBhavsar.]]></description>
      <pubDate>Mon, 19 May 2025 15:47:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549286</guid>
    </item>
    <item>
      <title>Where a glance is an action : drilling visual routines in driver training</title>
      <link>https://trid.trb.org/View/2534249</link>
      <description><![CDATA[Drivers rely on vision to navigate and interplay with other road users. However, the experienced drivers use of vision differs considerably from the novice's, with the former having developed what is essentially a “professional vision”. This compilation thesis seeks to investigate how the ability to see and act in methodic ways is taught and learned in driver training. It takes a special interest in trajectories of learning as displayed in and through participants' interaction.]]></description>
      <pubDate>Fri, 04 Apr 2025 15:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534249</guid>
    </item>
    <item>
      <title>Identifying High-Risk Situations in Right-Turn Phase of Heavy-Duty Vehicles Using Naturalistic Driving Data</title>
      <link>https://trid.trb.org/View/2518915</link>
      <description><![CDATA[The interaction between heavy-duty vehicles turning right and non-motor vehicles going straight has led to severe traffic crashes. It is essential to evaluate the driving risk of heavy-duty vehicles in the right-turn phase. Increasingly, studies have explored some indicators associated with driving risk. Based on naturalistic driving data of 121 heavy-duty vehicles in Nanjing, this research combined factor analysis and K-means cluster algorithm to assess the driving risk of two scenarios, one without a blind spot warning and another with a blind spot warning during the right-turn phase. The results have concluded the driving characteristics of heavy-duty vehicles under different risk levels. It formed a set of driving risk level assessment methods for heavy-duty vehicles in the right-turn phase. This evaluation method is expected to identify high-risk right-turn behaviors of heavy-duty vehicles and provide some insights to practitioners for traffic management.]]></description>
      <pubDate>Mon, 17 Mar 2025 09:18:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2518915</guid>
    </item>
    <item>
      <title>Dash Mount Flat-Panel mmWave Antenna With Blind Spotless 2-Dimensional Coverage</title>
      <link>https://trid.trb.org/View/2506172</link>
      <description><![CDATA[A new flat-panel dash mount array antenna with wide-beamwidth fan-beam steering to provide hemispherical mmWave vehicle-to-everything (V2X) communication is proposed in this article. Unlike the typical patch-based phased array antennas with narrow beamwidth and limited scan angles, the proposed antenna can ensure blind spotless communication coverage in both elevation and azimuth angles, despite of its placement on a planar dash board. To verify the proposed dash mount array antenna, a single element was first fabricated and measured at 28 GHz. The measured half power beamwidth was 148.3° and 141.3° in E- and H- planes, respectively. Then, the proposed antenna was extended to a 1 × 16 phased array configuration and verified to have ±80° scan coverage in both elevation and azimuth angles. Furthermore, the proposed array antenna was placed on the vehicular dashboard to validate the blind-spotless mmWave communication with a 64-QAM over-the-air (OTA) test. The measured error vector magnitude (EVM) at 28 GHz was 7.5% with the incident signal angle at 80°, proving the feasibility of blind-spotless flat-panel antenna.]]></description>
      <pubDate>Thu, 27 Feb 2025 10:47:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2506172</guid>
    </item>
    <item>
      <title>Workplace road safety education: truck safety and blind spots awareness project</title>
      <link>https://trid.trb.org/View/2509196</link>
      <description><![CDATA[Promoting road safety within our organization of 1,800 employees is a paramount. Through comprehensive workplace education efforts, we embed a culture of safety, cultivating heightened awareness among employees and encouraging responsible behavior on the roads, thus mitigating the risk of work-related incidents. This initiative is designed for long term impact, with a focus on continually reinforcing safety practices and adapting content to address current road safety issues. By tailoring education to the specific needs of our employees, we intent to also extend the benefits of road safety awareness to the broader community, acknowledging their integral role within families and networks. The aim is that increasing road safety awareness not only benefits our employees but also contributes to safer roads in the community in general.]]></description>
      <pubDate>Thu, 13 Feb 2025 09:05:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2509196</guid>
    </item>
  </channel>
</rss>