Roadway Information Research Articles

Digital modelling method of coal-mine roadway based on millimeter-wave radar array

The roadway space of coal mine is narrow, and the illumination is low and uneven. Dynamic mining is accompanied by dust and water mist. The accuracy and reliability of roadway data collected by vision and laser sensors are poor. Based on this, a digital modeling method of coal mine roadway based on millimeter-wave radar array is proposed. Firstly, aiming at the problem of complex environmental interference, combined with the characteristics of small amount of single frame data of millimeter-wave point cloud, a multi-layer filtering noise reduction processing and dynamic subgraph registration method of millimeter-wave point cloud is proposed to filter out interference points and realize single radar point cloud registration. Secondly, aiming at the problem that a single millimeter-wave radar cannot scan the complete roadway information at one time, combined with the characteristics of small elevation field of view of millimeter-wave radar, a millimeter-wave radar array acquisition system is built, and an improved iterative closest point (ICP) registration algorithm based on point cloud features is established to construct the roadway point cloud fusion model. Finally, aiming at the problem of uneven and sparse point cloud after array fusion, a Poisson surface reconstruction method based on point cloud density weighted interpolation is proposed to refine the roadway structure and realize the accurate reconstruction of digital roadway model. The experimental results show that the digital modeling method of millimeter-wave radar array can accurately obtain the information of roadway surrounding rock, the density of roadway point cloud is increased by 22.4%, and the average absolute error percentage of the width and height of the reconstructed roadway model is only 0.82% and 0.72%, which provides a new research method for the reconstruction of underground roadway and an important basis for the digital modeling method of coal mine roadway.

Automatically detect crosswalks from satellite view images – A deep learning approach with ground truth verification

Like roadway information is to motor vehicle safety, pedestrian facility information (e.g., sidewalk presence) is crucial towards improving the safety of these vulnerable road users. Yet unlike widely accessible roadway data, pedestrian facility data is unavailable for most state agencies. Without this information, data-driven problem identification, countermeasure analysis, project evaluation, and performance management will be heavily impeded. Thus, urgent need for this data was recognized by state departments of transportation (DOTs). To address this need, we developed an automated approach to automatically detect crosswalks from satellite view images. The most advanced deep learning methodology, transfer learning with a Convolutional Neural Network (CNN) was used to handle real-world images. During the prediction process, a satellite image of a roadway pavement was analyzed by the satellite view model to predict the presence of a crosswalk. Then, the street view image of the same target was detected by the integrated street view model as a ground truth check. A total of 18,361 images from Bing Maps in satellite view and street view were used to train and test the deep learning model. As a result, the satellite view model itself achieved 98.43% accuracy using testing data from the same region. When dealing with data from another region, using the satellite view detection with ground truth checking increased the accuracy by 49%. It is obvious that by integrating the ground truth checking model into the satellite view crosswalk detection, we can obtain a more robust model which can handle highly occluded, low quality satellite images.

Exploring Crowdsourced Hard—Acceleration and Braking Event Data for Evaluating Safety Performance of Low-Volume Rural Highways in Iowa

There are over four million miles of two-lane roadways across the United States, of which a substantial portion is low-volume roads (LVR). Traditionally, most traffic safety efforts and countermeasures focus on high-volume high-crash urban locations. This is because LVRs cover an extensive area, and the rarity of crashes makes it challenging to use crash data to monitor the safety performance of LVRs regularly. In addition, obtaining up-to-date roadway information, such as pavement or shoulder conditions of an extensive LVR network, can be exceptionally difficult. In recent times, crowdsourced hard-acceleration and braking event data have become commercially available, which can provide precise geolocation information and can be readily acquired from different vendors. The present paper examines the potential use of this data to identify opportunities to monitor the safety of LVRs. This research examined approximately 12 million hard-acceleration and hard-braking events over a 3-months period and 26,743 crashes, including 9373 fatal injuries over the past 5-year period. The study found a moderate correlation between hard acceleration/hard-braking events with historical crash events. This study conducted a hot spot analysis using hard-acceleration/hard-braking and crash datasets. Hotspot analysis detected spatial clusters of high-risk crash locations and detected 848 common high-risk sites. Finally, this paper proposes a combined ranking scheme that simultaneously considers historical crash events and hard-acceleration/hard-braking events. The research concludes by suggesting that agencies can potentially use the hard-acceleration and hard-braking event dataset along with the historical crash dataset to effectively supervise the safety performance of the vast network of LVRs more frequently.

Deformation Prediction and Analysis of Soft Rock Roadway with High Altitude and Large Buried Depth Based on Particle Swarm Optimization LSTM Model

Deformation prediction is an important basis for roadway information construction, especially for soft rock roadway at high altitude and large buried depth, whether the deformation of roadway surrounding rock can be effectively and accurately predicted is an important basis for judging the stability of roadway surrounding rock. However, at present, the research on the informatization construction of the roadway is not in-depth, and the intelligent prediction technology for the deformation of the surrounding rock of the roadway is still in its infancy, and the accuracy of deformation prediction is also low. Therefore, based on the research of domestic and foreign researchers, in order to solve the breakthrough of related technology, this paper puts forward the deformation prediction and control technology of high altitude and deep buried soft rock roadway based on a neural network model. This method is based on the traditional prediction model and is replaced by the neural network, so as to improve the problems of low accuracy and large prediction deviation in the related deformation prediction of the traditional prediction model. At the same time, aiming at the problem of poor local weight and network search ability, an improved method using particle swarm optimization algorithm is proposed, which effectively considers the influence of local and global factors on the combined weight. Finally, the improved deformation prediction model of high altitude and deep buried soft rock roadway based on particle swarm optimization LSTM model is applied to an engineering example and compared with the traditional model to explore its feasibility and effectiveness. The results show that the prediction model has higher prediction accuracy than the traditional prediction model, and the relative deviation of the prediction results is controlled within 2%. At the same time, compared with other models (BP neural network model), it has relatively higher accuracy and stability. The research results can provide a new idea for the deformation prediction of soft rock roadway with high altitude and deep burial.

Evaluating Safety Benefits of Vehicle-to-Everything Sensor Sharing on Rural Highways Using the Microscopic Simulation Model

Safety is a critical aspect of transportation design and operations. Practitioners utilize various references to ensure that roadways meet safety, operational, and sustainability requirements. Despite this, human error remains as a contributing factor toward unsafe driving behavior and potential crashes. Connected and autonomous vehicles (CAVs) have the potential to enhance traffic safety and operations. Although sensor perception ranges and capabilities pose challenges, the sharing of information via Vehicle-to-Everything (V2X) communication provides CAVs with a potential solution for overcoming sensor limitations. The objective of this study is to use the Simulation of Urban Mobility software to assess safety impacts when using V2X to share sensor-obtained roadway information with a CAV. To this end, this study proposes a novel method for simulating driver behavior that combines car following with consideration of the roadway’s geometric configuration. Several scenarios are utilized to observe the behavior of simulated drivers on a straight tangent approaching a sharp horizontal curve. This study evaluates driver performance using the measured values for longitudinal jerk, lateral jerk, and speed variance. The results of this study indicate that V2X sensor sharing can provide significant benefits to CAV performance and can reduce the safety risk. CAVs receiving sensor-obtained information behave in a manner more akin to their human-driven counterparts in comparison to those receiving basic safety messages. CAVs using sensor-obtained information maintain braking and lateral jerk values within safety thresholds. In addition, speed variance was at its lowest when CAVs utilized V2X sensor information.

Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data

Changing lane is a complex driving maneuver and could have a significant effect on traffic safety. Therefore, developing accurate and timely lane change detection systems could assist drivers to perform and complete this complicated driving task safely. This study proposes reliable lane change detection models based on deep learning (DL) and including features from vehicle kinematics, machine vision, roadway geometries, and driver demographics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. A cutting-edge technique named “DeepInsight” was applied to transform numeric lane change data into image data. The generated image data sets were trained, validated, and tested using a novel DL architecture called ResNet-18, considering six categories of features. To balance the lane change database, two data balancing methods—the synthetic minority oversampling technique (SMOTE) and random majority under sampling (RMUS)—were considered and tested with various sampling ratios. In addition, wrapper-based Boruta and eXtreme gradient boosting (XGBoost) algorithms were used to extract relevant features in each category. A recall of 82% and overall accuracy of 77.9% were found using a ratio of 1:1 for the model based on vehicle kinematic features suggesting that the developed model could be used in the absence of other data. However, the best detection performance was observed using the same ratio for a reduced model based on XGBoost, which produced a recall and overall accuracy of 98.8% and 95%, respectively, considering all the features. The proposed detection system could be effectively leveraged to monitor lane change behavior and provide appropriate control strategies in a connected vehicle (CV) environment.

Identification of pole-like objects from mobile laser scanning data of urban roadway scene

The information acquisition about pole-like objects (PLOs) situated along the road is important in roadway inventory related studies, such as road safety analysis and street visibility studies for traffic management. To collect the detailed roadway information, mobile laser scanning (MLS) system has been adopted as a mainstream tool since last decade. In this paper, a machine learning-based approach using random forest is proposed to identify PLOs in MLS data of roadway scene. First, ground points are removed from the input data thereafter the non-ground points are clustered into cluster features. The random forest-based model is trained using dimension and Eigen values-based feature variables derived from the training samples of various PLOs and non-PLOs features manually extracted from the training MLS data. The proposed method was tested on two different MLS datasets, which were acquired along 4.2 and 2 km long urban roadway environment having perfect as well as complex roadway scenes. Both, simple as well as complex PLOs were successfully identified in these datasets and an average correctness and completeness were obtained 97.67% and 97.79%, respectively. Therefore, the proposed approach has potential to deliver promising results in perfect as well as complex roadway surroundings.

Leveraging gradient weighted class activation mapping to improve classification effectiveness: Case study in transportation infrastructure characterization

Roadway “corners” are common for pedestrian use, whether designated with markings or not. Different types of markings have been deployed, ranging from simple parallel lines to more complex designs. Understanding the impact of different types of crosswalks is important for public safety. In this work we explore methods to improve the logging of marked crosswalk types. We used the Roadway Information Database from the Second Strategic Highway Research Project and used active learning methods with transfer learning to identify the crosswalk types (marked or unmarked). Upon completion we found our classifiers were unable to perform above roughly 94% correct classifications. To improve their efficacy, we separated the crosswalks into their “fine grained” types and used Gradient-Weighted Class Activation Mapping to isolate and study the features that classified the crosswalks. We compared this with sampled manually marked crosswalks and present findings. We believe this use case can represent a process to improve the active learning method for some visual machine learning applications.

Vehicle Model Predictive Trajectory Tracking Control with Curvature and Friction Preview

Autonomous vehicle trajectory tracking control is challenged by situations of varying road surface friction, especially in the scenario where there is a sudden decrease in friction in an area with high road curvature. If the situation is unknown to the control law, vehicles with high speed are more likely to lose tracking performance and/or stability, resulting in loss of control or the vehicle departing the lane unexpectedly. However, with connectivity either to other vehicles, infrastructure, or cloud services, vehicles may have access to upcoming roadway information, particularly the friction and curvature in the road path ahead. This paper introduces a model-based predictive trajectory-tracking control structure using the previewed knowledge of path curvature and road friction. In the structure, path following and vehicle stabilization are incorporated through a model predictive controller. Meanwhile, long-range vehicle speed planning and tracking control are integrated to ensure the vehicle can slow down appropriately before encountering hazardous road conditions. This approach has two major advantages. First, the prior knowledge of the desired path is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the controller to avoid violation of tire force limits while keeping performance and stability guarantees. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a sharply curving road with varying friction conditions, with results showing that the controller can drive a vehicle up to the handling limits and track the desired trajectory accurately.

Empirical Bayes application on low-volume roads: Oregon case study

Introduction: This paper investigates the Empirical Bayes (EB) method and the Highway Safety Manual (HSM) predictive methodology for network screening on low-volume roads in Oregon. Method: A study sample of around 870 miles of rural two-lane roadways with extensive crash, traffic and roadway information was used in this investigation. To understand the effect of low traffic exposure in estimating the EB expected number of crashes, the contributions of both the observed and the HSM predicted number of crashes were analyzed. Results and Conclusions: The study found that, on low-volume roads, the predicted number of crashes is the major contributor in estimating the EB expected number of crashes. The study also found a large discrepancy between the observed and the predicted number of crashes using the HSM procedures calibrated for the state of Oregon, which could partly be attributed to the unique attributes of low-volume roads that are different from the rest of the network. However, the expected number of crashes for the study sample using the HSM EB method was reasonably close to the observed number of crashes over the 10-year study period. Practical Applications: Based on the findings, it can still be very effective to use network screening methods that rely primarily on risk factors for low-volume road networks. This is especially applicable in situations where accurate and reliable crash data are not available.

Effect of Various Speed Management Strategies on Bicycle Crashes for Urban Roads in Central Florida

In recent years, cycling has become an increasingly popular transportation mode around the world. In comparison with other popular modes of transportation, cycling is economical and energy efficient. While many studies have been conducted for the analysis of bicycle safety, most were limited in bicycle exposure data and on-street data. This study tries to improve the current safety performance functions for bicycle crashes at urban corridors by utilizing crowdsource data from STRAVA and on-street speed management strategies data. Speed management strategies are any roadway alterations that cause a change in motorists’ driving behavior. In Florida, these speed management strategies are defined by the Florida Department of Transportation design manual. Considering the disproportionate representation of cyclists from the STRAVA data, adjustments were made to represent more accurately the cyclists based on the video detection data by developing a Tobit model. The adjusted STRAVA data was used for bicyclist exposure to analyze bicycle crashes on urban arterials. A Bayesian joint model was developed to identify the relations between the bicycle crash frequency and factors relating to speed management strategies. Other factors, such as vehicle traffic data, roadway information, socio-demographic characteristics, and land use data, were also considered in the model. The results suggest that the adjusted STRAVA data could be used as the exposure for bicycle crash analysis. The results also highlight the significant effects of speed management strategies, such as parking lots and surface pavement. It is expected that these findings could help engineers develop effective strategies to enhance safety for bicyclists.

Effects of Fog in a Brazilian Road Segment Analyzed by a Driving Simulator for Sustainable Transport: Drivers’ Visual Profile

Visibility is a critical factor for drivers to perceive roadway information, and fog is an inclement weather condition that directly impacts their vision, since it reduces both overall contrast and visibility of the driving scene. Visual attention has been considered a contributing factor to traffic crashes, and fog-related accidents are prone to be more severe and involve multiple vehicles. The literature lacks studies on the influence of fog on drivers’ visual performance and environment’s infrastructure design. This article investigates the effects of fog on drivers’ performance in a Brazilian curved road segment through a driving simulator experiment – more precisely, whether the presence of fog (foggy scenario) or its absence (clear scenario) significantly affects the visual profile. In the foggy scenario, the results showed the tracked area was concentrated in a smaller region, despite an increase in the number of fixations compared with the clear scenario. The fixation duration did not change between the scenarios and the pupil dilation was shorter in the foggy one. The study shows the influence of environmental conditions on the driver’s performance and is one of the first on the use of driving simulators with realistic representations of the road infrastructure and its surrounding for the understanding of driving under fog in the Brazilian scenario. Besides roadway geometry elements, driving simulator studies enable analyses of features related to the interaction between route environment and driver’s answer, and can improve safety in places with visibility problems caused by fog, reducing their environmental impact and preserving drivers’ lives.

Machine Learning Approach for Predicting Lane-Change Maneuvers using the SHRP2 Naturalistic Driving Study Data

Accurate lane-change prediction information in real time is essential to safely operate Autonomous Vehicles (AVs) on the roadways, especially at the early stage of AVs deployment, where there will be an interaction between AVs and human-driven vehicles. This study proposed reliable lane-change prediction models considering features from vehicle kinematics, machine vision, driver, and roadway geometric characteristics using the trajectory-level SHRP2 Naturalistic Driving Study and Roadway Information Database. Several machine learning algorithms were trained, validated, tested, and comparatively analyzed including, Classification And Regression Trees (CART), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), K Nearest Neighbor (KNN), and Naïve Bayes (NB) based on six different sets of features. In each feature set, relevant features were extracted through a wrapper-based algorithm named Boruta. The results showed that the XGBoost model outperformed all other models in relation to its highest overall prediction accuracy (97%) and F1-score (95.5%) considering all features. However, the highest overall prediction accuracy of 97.3% and F1-score of 95.9% were observed in the XGBoost model based on vehicle kinematics features. Moreover, it was found that XGBoost was the only model that achieved a reliable and balanced prediction performance across all six feature sets. Furthermore, a simplified XGBoost model was developed for each feature set considering the practical implementation of the model. The proposed prediction model could help in trajectory planning for AVs and could be used to develop more reliable advanced driver assistance systems (ADAS) in a cooperative connected and automated vehicle environment.

A RASPBERRY PI SELF-DRIVING CART BASED ON OPENCV AND DEEP LEARNING .

The self-driving trolley created in this thesis uses cameras and ultrasonic sensors to obtain roadway information, and a deep learning based target recognition algorithm to find out which are the targets in the data obtained, so that the trolley can drive itself on a simulated roadway with functions such as obstacle avoidance and traffic signal recognition. Originally the car used a Raspberry Pi 3b+, but here the jetson nano, which is better than the Raspberry Pi 3b+, is used to implement it.

ОСОБЕННОСТИ ПРОЕКТИРОВАНИЯ ВЕЛОДОРОЖЕК В САНКТ-ПЕТЕРБУРГЕ НА ОСНОВЕ АНАЛИЗА ОПЫТА СТРАН БАЛТИЙСКОГО РЕГИОНА

The article deals with statistics on the development of Bicycle roads in Russia and in the world, as well as design methods for a specific section of the connection of Bicycle routes in St. Petersburg. The article discusses the experience of using and entering bike paths based on the experience of Finland, as well as the types of bike paths and infrastructure features for metropolises. A model for creating a bike path by partially narrowing the roadway, graphical functions, and analytical information are provided. Practical examples of changing the infrastructure for bike paths are given. Keywords: bike path, traffic volume, design the roadway, lane width.

Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

Modeling crash risk of horizontal curves using large-scale auto-extracted roadway geometry data

Highway horizontal curves (H-curves) provide a smooth transition between two tangent sections of roadways. They allow vehicles to adjust their travel directions gradually. However, the geometry changes of the highway sections with H-curves often raise safety concerns. In order to deploy effective safety countermeasures, a critical task is to understand the risk factors associated with H-curves. Existing studies have made efforts to probe the safety issues associated with H-curves, whereas they were limited to relatively small-scale examinations because of the challenges in identifying H-curves from large road networks. In addition, due to the lack of well-archived traffic and roadway information, gathering other data associated with the H-curves is also difficult. Regarding to these gaps, this study aims to leverage open-source data to analyze the crash risk of highway sections with H-curves. In particular, the present study highlights itself from two main aspects: (i) a H-curve extraction tool was developed to facilitate large-scale curve data collection through the analytics of different open source data; and (ii) a crash modeling framework was developed to quantify H-curve crash risk. A case study based on a statewide road network was performed to test the developed crash risk models with the collected curve data. The results show the opportunities of using the developed tool for large-scale data collection and analyze the safety impacts of H-curve geometric properties, elevation change, traffic exposure, among others. Findings of this study provide insights into the improvement of H-curve data collection and safety evaluation.

Longwall Retreat and Creep Measurement Based on UWB Radar Imaging Method

The measurement of longwall retreat and creep displacements is of great significance for the safety production of a coal mine. In order to reliably and accurately obtain the longwall retreat and creep displacements, this paper proposes an ultrawideband (UWB) radar imaging method. The bolt plates fixed in the roadway coal wall are imaged as the target points. First, a signal model is built, and a modified nonlinear chirp scaling (NLCS) imaging algorithm is designed to obtain the high-resolution image. Then, the retreat and creep displacements are estimated based on the imaging results. Finally, simulation and experiment are performed. The simulation results show that the radar imaging method is theoretically feasible to measure the retreat and creep displacements. The measurement errors in the experiment are 0.058 m and 0.040 m, respectively. In the experiment, the radar velocity fluctuation and signal attenuation cause the target azimuth and range errors in the imaging results, which makes the measurement errors in the experiment larger than that in the simulation. This method requires no additional roadway information except for the bolt plates fixed in the coal wall. In addition, the signal propagation of UWB radar is rarely affected by the dust and moisture in the harsh environment.

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques

Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks or provide assistance when it is needed the most. This study proposed trajectory-level lane change detection models based on features from vehicle kinematics, machine vision, roadway characteristics, and driver demographics under different weather conditions. To develop the models, the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) datasets were utilized. Initially, descriptive statistics were utilized to investigate the lane change behavior, which revealed significant differences among different weather conditions for most of the parameters. Six data fusion categories were introduced for the first time, considering different data availability. In order to select relevant features in each category, Boruta, a wrapper-based algorithm was employed. The lane change detection models were trained, validated, and comparatively evaluated using four Machine Learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtrem Gradient Boosting (XGBoost). The results revealed that the highest overall detection accuracy was found to be 95.9 % using the XGBoost model when all the features were included in the model. Moreover, the highest overall detection accuracy of 81.9 % using the RF model was achieved considering only vehicle kinematics-based features, indicating that the proposed model could be utilized when other data are not available. Furthermore, the analysis of the impact of weather conditions on lane change detection suggested that incorporating weather could improve the accuracy of lane change detection. In addition, the analysis of early lane change detection indicated that the proposed algorithm could predict the lane changes within 5 s before the vehicles cross the lane line. The developed detection models could be used to monitor and control driver behavior in a Cooperative Automated Vehicle environment.

Human factors evaluation of an in-vehicle active traffic and demand management (ATDM) system

This research study focused on the evaluation of an emulated in-vehicle Active Traffic and Demand Management (ATDM) system on Interstate 66 in Northern Virginia. Traditional ATDM systems rely on infrastructure-mounted variable message signs (VMS) to provide information (speed limits, lane availability, etc.) to the traveling public. By providing information about dynamic roadway conditions on an in-vehicle device, the ATDM may improve driving safety and performance by allowing drivers to remain consistently aware of forthcoming traffic conditions and roadway requirements; even when external signage is inaccessible. This study represents an initial investigation of an emulated in-vehicle ATDM to provide developers with design guidance and ensure that unintended consequences, such as distraction, do not undermine the potential benefits.Twenty younger and 20 older participants, accompanied by a member of the research team, experienced the following ATDM features on an in-vehicle device (IVD) mounted to the dashboard: (1) dynamic speed limits, (2) dynamic lane use/shoulder control, (3) High Occupancy Vehicle (HOV) restrictions, and (4) roadway information from variable message signs. The IVD was equipped with auditory and visual alerts notifying the driver when relevant visual information was updated. Research questions addressed distraction and driver behavior associated with use of the system. Qualitative and quantitative participant data was acquired from the instrumented vehicle, various questionnaires, and researcher observation.Several key findings were uncovered: (1) The IVD, as tested, did not warrant classification as a source of distraction according to the NHTSA guidelines; v2) There was a significant difference in eye-glance durations to the IVD when comparing the VMS alerts to both the speed limit and lane management alerts; and (3) The speed limit alert motivated participants to alter their speed (per survey results and participant speed data).