Search Results (48)

GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is required for lane-level positioning, route generation, and navigation. However, in environments where GPS signals are blocked, such as tunnels and underground roads, absolute positioning is impossible. Instead, relative positioning methods integrating IMU, IVN, and cameras are used. These methods are influenced by numerous variables, however, such as vehicle speed and road conditions, resulting in lower accuracy. In this study, we conducted experiments on current vehicle navigation technologies using an autonomous driving simulation vehicle in the Suri–Suam Tunnel of the Seoul Metropolitan Area 1st Ring Expressway. To recognize objects (lane markings/2D/3D) for position correction inside the tunnel, data on tunnel and underground road infrastructure in Seoul and Gyeonggi Province was collected, processed, refined, and trained. Additionally, a Loosely Coupled-based Kalman Filter was designed and applied for the fusion of GPSs, IMUs, and IVNs. As a result, an error of 113.62 cm was observed in certain sections. This suggests that while the technology is applicable for general vehicle lane-level navigation in ultra-long tunnels spanning several kilometers for public service, it falls short of meeting the precision required for autonomous driving systems, which demand lane-level accuracy. Therefore, it was concluded that infrastructure-based absolute positioning technology is necessary to enable precise navigation inside tunnels. Full article

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems. Full article

This study proposes a vision-based framework to support AVs in maintaining stable lane-keeping by assessing the condition of lane markings. Unlike existing infrastructure standards focused on human visibility, this study addresses the need for criteria suited to sensor-based AV environments. Using real driving data from urban expressways in Seoul, a YOLOv5-based lane detection algorithm was developed and enhanced through multi-label annotation and data augmentation. The model achieved a mean average precision (mAP) of 97.4% and demonstrated strong generalization on external datasets such as KITTI and TuSimple. For lane condition assessment, a pixel occupancy–based method was applied, combined with Canny edge detection and morphological operations. A threshold of 80-pixel occupancy was used to classify lanes as intact or worn. The proposed framework reliably detected lane degradation under various road and lighting conditions. These results suggest that quantitative, image-based indicators can complement traditional standards and guide AV-oriented infrastructure policy. Limitations include a lack of adverse weather data and dataset-specific threshold sensitivity. Full article

The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts. Full article

The application of intelligent and connected technologies, such as vehicle-to-everything (V2X), profoundly influences car-following behavior and traffic flow characteristics. While empirical studies have demonstrated that the car-following behavior is affected by the vehicles in the adjacent lanes, there is no car-following model that comprehensively incorporates the leading and following neighboring vehicles, including those in the adjacent lanes. Under the conditions of intelligent and connected technologies penetration, the information regarding the aforementioned vehicles can be accessed and applied in the car-following process. However, the absence of the corresponding car-following model limits the understanding of traffic flow characteristics under this condition, particularly concerning critical stability characteristics. To address this research gap, a new car-following model is proposed, which integrates the neighboring leading vehicles in the current and adjacent lances, marked as the surrounding leading vehicle (SLV), and the rear vehicle in the current lane. The linear stability analysis and nonlinear analysis of the proposed model, as well as the numerical simulation of the propagation process of disturbance in the vehicle fleet, are conducted. Based on this, the stability and evolution characteristics of the traffic flow are explored. The results of theoretical and simulation analysis consistently suggest that the integration of the motion state information of the SLV and the rear vehicle can effectively stabilize the traffic flow, which means that traffic congestion can be alleviated and transportation efficiency will be improved. This research can provide references for the research fields including traffic flow theory and is of significant importance for alleviating and mitigating traffic congestion under the condition of intelligent and connected vehicle (CAV) penetration. Full article

This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping. Full article

Lane detection is a critical component of autonomous driving assistance systems (ADASs), playing a pivotal role in ensuring road safety and orderly vehicle movement. In regions like Southeast Asia, characterized by high rainfall, the challenge of detecting lane markers is exacerbated by blurred markings and road surface issues such as potholes. This research addresses the problem of multi-class lane marker detection under rainy conditions, essential for ADASs to maintain safe and compliant vehicle operations. Using a deep learning approach, the proposed model was trained and tested on the Berkeley Video Dataset, incorporating various weather conditions, including rain. The methodology included 150 training epochs executed through Roboflow, with results visualized on the Wandb platform. The model successfully identified five classes of lane markers, namely dashed, single lane, double lane, none, and zebra crossings, demonstrating robust performance in challenging conditions. Evaluation metrics, including train/box_loss and train/cls_loss, showcased significant improvements, with both loss metrics stabilizing below 1.0 after training, indicating accurate bounding box predictions and classification. The findings support advancements in ADASs, enhancing road safety and fostering a more secure and orderly traffic environment during adverse weather. Full article

Road markings, such as lane dividers and pedestrian crossings, are integral in ensuring the safety of road users. However, traditional markings frequently exhibit limitations, including short lifespans, diminished visibility, and significant maintenance costs, particularly as traffic volumes increase. To address these persistent challenges, this study presents a thermoplastic road marking system that combines material innovation and advanced application techniques. Central to this approach is the portable heating system, equipped with ceramic heaters and precise temperature controls, which facilitates uniform heating while mitigating fire risks. The thermoplastic blend, processed into pre-formed sheets, was integrated with this heating technology. Together, these components enabled a two-phase process, engraving asphalt surfaces followed by sheet integration, that ensured robust adhesion and seamless bonding. Field trials conducted on various asphalt types validated the system’s reliability, demonstrating its durability under traffic loads and consistent visibility. By integrating durable materials with advanced application methods, this methodology significantly enhances the efficiency, longevity, and safety of road markings. It presents a practical and scalable solution for modern infrastructure needs. Future research will focus on evaluating the system’s long-term performance under extreme weather conditions to further optimize its applicability. Full article

This paper explores an interesting approach to wind energy technology, focusing on a novel type of drag-driven vertical-axis wind turbines (VAWTs). Studied geometries employ rotor-shaped cross-sections, presenting a distinctive approach to harnessing wind energy efficiently. The rotor-shaped cross-section geometries are examined for their aerodynamic efficiency, showcasing the meticulous engineering behind this innovation. The drag-driven turbine shapes are analyzed for their ability to maximize energy extraction in a variety of wind conditions. A significant aspect of these turbines is their adaptability for diverse applications. This article discusses the feasibility and advantages of utilizing these VAWTs in fence configurations, offering an innovative integration of renewable energy generation with physical infrastructure. The scalability of the turbines is highlighted, enabling their deployment as a fence around residential properties or as separators between highway lanes and as energy-generating structures atop buildings. The scientific findings presented in this article contribute valuable insights into the technological advancements of rotor-shaped VAWTs and their potential impact on decentralized wind energy generation. The scalable and versatile nature of these turbines opens up new possibilities for sustainable energy solutions in both urban and residential settings, marking a significant step forward in the field of renewable energy research and technology. In particular, it was shown that among the proposed rotor geometries, the five-blade rotor was characterized by the highest efficiency and, working in a palisade configuration with a spacing of 10 mm to 20 mm, produced higher average values of the torque coefficient than the corresponding Savonius turbine. Full article

The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. Full article

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles. Full article

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded $\pm 0.016$ m⁻¹. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs. Full article

Freight transportation, dominated by trucks, is an integral part of trade and production in the USA. Given the prevalence of large truck crashes, a comprehensive investigation is imperative to ascertain the underlying causes. This study analyzed 2017–2021 Texas crash data to identify factors impacting large truck crash rates and injury severity and to locate high-risk zones for severe incidents. Logistic regression models and bivariate analysis were utilized to assess the impacts of various crash-related variables individually and collectively. Heat maps and hotspot analysis were employed to pinpoint areas with a high frequency of both minor and severe large truck crashes. The findings of the investigation highlighted night-time no-passing zones and marked lanes as primary road traffic control, highway or FM roads, a higher posted road speed limit, dark lighting conditions, male and older drivers, and curved road alignment as prominent contributing factors to large truck crashes. Furthermore, in cases where the large truck driver was determined not to be at fault, the likelihood of severe collisions significantly increased. The study’s findings urge policymakers to prioritize infrastructure improvements like dual left-turn lanes and extended exit ramps while advocating for wider adoption of safety technologies like lane departure warnings and autonomous emergency braking. Additionally, public awareness campaigns aimed at reducing distracted driving and drunk driving, particularly among truck drivers, could significantly reduce crashes. By implementing these targeted solutions, we can create safer roads for everyone in Texas. Full article

The vast majority of autonomous driving systems are limited to applications on roads with clear lane markings and are implemented using commercial-grade sensing systems coupled with specialized graphic accelerator hardware. This research reviews an alternative approach for autonomously steering vehicles that eliminates the dependency on road markings and specialized hardware. A combination of machine vision, machine learning, and artificial intelligence based on popular pre-trained Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) was used to drive a vehicle along roads lacking lane markings (unmarked roads). The team developed and tested this approach on the Autonomous Campus Transport (ACTor) vehicle—an autonomous vehicle development and research platform coupled with a low-cost webcam-based sensing system and minimal computational resources. The proposed solution was evaluated on real-world roads and varying environmental conditions. It was found that this solution may be used to successfully navigate unmarked roads autonomously with acceptable road-following behavior. Full article

In order to find optimized headlight distributions based on real traffic data, a three-step approach is chosen. Since the complete investigations are too extensive to fit into a single publication, this paper is the first in a series of three publications. Over three papers, a novel way to optimize automotive headlight distributions based on real-life traffic and eye-tracking data is presented, based on 119 test subjects who participated in over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances, and other objects in traffic. In the present paper, a baseline headlight distribution is derived from a series of detection tests conducted under ideal conditions, with a total of three tests, each with 19–30 subjects, conducted within the same test environment. In the first test, the influence of low beam intensity on the detection of pedestrians on the sidewalk ($5.0$ $\mathrm{m}$ from the center of the driving lane) is investigated. In the second test, the influence of different high beam intensities was investigated for the same detection task. In the third test, the headlight distribution and intensity are kept constant at a representative high beam level, but the detection task is changed. In this test, the pedestrian detection target is placed along different detection angles, ranging from immediately adjacent to the road ($2.5$${}^{°}$) to $15.5$ $\mathrm{m}$ away from the center of the driving lane ($8.0$${}^{°}$). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a $1.2$ km long straight road and normal road markings, but without oncoming traffic, tasks other than keeping the vehicle with cruise control within its lane, or other distracting objects present. The tests yielded two sets of data; the first is the intensity, based on the first two studies, needed to ensure sufficient intensity to detect objects under ideal conditions at distances needed for different driving speeds. The last test then uses these intensities and necessary variations in the required intensity to create an idealized, symmetric headlight distribution as a baseline for subsequent publications. Although the distribution applies only to passenger vehicles like the one used in the test, the same approach could be applied to other vehicle types. The second paper of this series will focus on real traffic objects and their distributions within the traffic space in order to identify relevant areas in headlight distribution when driving under real traffic conditions. The third paper of this series will analyze driver gaze distributions during real driving scenarios. The data from all three papers are used to create optimized headlight distributions, thereby showing how such an optimized distribution relates to current headlight distributions in terms of luminous flux, intensity, and overall distribution. Full article