Lane Width Estimation in Work Zones Using LiDAR-Based Mobile Mapping Systems

Traffic data were collected from 11 work zones on Interstate highways in Illinois in which one of the two lanes was open. The reductions in free-flow speed (FFS) due to narrow lanes and lateral clearances in work zones were studied. It was found that the reductions in FFSs of vehicles in work zones because of narrow lanes were higher than the reductions given in the Highway Capacity Manual for basic freeway sections. The data also showed that the narrower the lane was, the greater the speed reduction was. The data showed that the FFSs of heavy vehicles were statistically lower than the FFSs of passenger cars, even though the speed limit was the same for both types of vehicles. In addition, the reduction in the FFSs of heavy vehicles was greater than the reduction in the FFSs of passenger cars. This greater reduction in the speed of heavy vehicles affected the performance of the traffic stream in work zones. Thus, it should be considered in the computation of the passenger car equivalence for heavy vehicles. It is recommended that 10, 7, 4.4, and 2.1 mph be used for speed reduction in work zones for lane widths of 10, 10.5, 11, and 11.5 ft, respectively.

Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization.

Mobile mapping systems are becoming increasingly popular as they can build 3D models of the environment rapidly by using a laser scanner that is integrated with a navigation system. 3D mobile mapping has been widely used for applications such as 3D city modelling and mapping of the scanned environments. However, accurate mapping relies on not only the scanner’s performance but also on the quality of the navigation results (accuracy and robustness) . This paper discusses the potentials of using 3D mobile mapping systems for landscape change detection, that is traditionally carried out by terrestrial laser scanners that can be accurately geo-referenced at a static location to produce highly accurate dense point clouds. Yet compared to conventional surveying using terrestrial laser scanners, several advantages of mobile mapping systems can be identified. A large area can be monitored in a relatively short period, which enables high repeat frequency monitoring without having to set-up dedicated stations. However, current mobile mapping applications are limited by the quality of navigation results, especially in different environments. The change detection ability of mobile mapping systems is therefore significantly affected by the quality of the navigation results. This paper presents some data collected for the purpose of monitoring from a mobile platform. The datasets are analysed to address current potentials and difficulties. The change detection results are also presented based on the collected dataset. Results indicate the potentials of change detection using a mobile mapping system and suggestions to enhance quality and robustness.

ABSTRACTLane and shoulder widths are important highway safety factors because wider lanes and shoulders generally help to reduce crashes. In practice however, due to physical limitations of the right-of-way or statutory restrictions, it may be the case that the existing overall width of the roadway (lanes plus shoulders) cannot exceed a certain value. For a given overall roadway width constraint, wider lanes mean there will be smaller space for shoulders, and vice versa. In such zero-sum situations, it is not certain if a cross-sectional configuration with wide lanes and narrow shoulders is safer compared with one with narrow lanes and wide shoulders. Clearly, for a given total roadway width (TRW), the trade-off between shoulder and lane widths should be evaluated in order to maximize the user benefits (safety) without unduly incurring excessive agency life-cycle costs of construction and preservation. The TRW allocations to the lane and shoulder, expressed as the ratio of lane width to shoulder width, will generally depend on the TRW; lane and shoulder pavement material types and costs; and traffic volume, among other factors. In order to optimize the lane and shoulder width allocations for a given TRW, this paper proposes a framework that formulates the problem as an optimization problem with the objective of minimizing the total (agency and user) life-cycle cost of the roadway. First, the relationships are established between lane/shoulder widths and the resulting user costs (crashes), and also between lane/shoulder widths and the associated agency costs of lane/shoulder construction and preservation. The sensitivity of the optimal solution to different evaluation inputs (such as the relative weights between the agency and user costs) is also analyzed. Using the developed framework, the paper presents a number of decision support charts that can be used by highway agencies to determine the optimal lane and shoulder widths under a given set of conditions including the highway functional class and the total available roadway width. The flexibility of the proposed optimization framework to accommodate risk compensation effects was also demonstrated.

The mobile 3D mapping systems (MMS) and the integration with Geographical Information Systems (GIS) represent an innovative and efficient approach to modern topography. It is recommended and useful for infrastructure analysis and urban planning. This modern technology provides accurate, fast, and detailed data, facilitating well-informed decision-making and contributing to the development of sustainable infrastructure in the 21st century. The purpose of this research was to use a mobile mapping system for land surveying measurement of an urban area, independent of pre-established ground control points. On the other hand, the research is supposed to demonstrate the usefulness of kinematic trajectory, digital imaging, and laser scanning in acquiring, storing, and processing point cloud data. This paper shows an affordable method and a modern scanning instrument used for mobile mapping scanning, to create a georeferenced point cloud (PC). The obtained result has centimetric precision of georeferenced point cloud data, even if the average speed of the car was about 30km/h. The trajectory adjustment with static GNSS measurements increases the accuracy of ground control points (GCPs) up to ±5cm for horizontal positioning and vertical positioning. Although this study was carried out while the system was mounted on a car the result followed the national accuracy requirements for land surveying and cadastre fields. In other words, the mobile mapping system is a faster and cost-effective in time alternative to static laser scanning and it is important to continue to explore and adopt these technological solutions to face the future challenges in the field of urban development and infrastructure.

Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data

In the medium-term, the number of semi-autonomous vehicles is expected to rise significantly. These changes in vehicle capabilities make it necessary to analyze their interaction with road infrastructure, which has been developed for human-driven vehicles. Current systems use artificial vision, recording the oncoming road and using the center and edgeline road markings to automatically facilitate keeping the vehicle within the lane. In addition to alignment and road markings, lane width has emerged as one of the geometric parameters that might cause disengagement and therefore must be assessed. The objective of this research was to study the impact of lane width on semi-autonomous vehicle performance. The automatic lateral control of this type of vehicle was tested along 81 lanes of an urban arterial comprising diverse widths. Results showed that the semi-autonomous system tended to fail on narrow lanes. There was a maximum width below which human control was always required—referred to as the human lane width—measuring 2.5 m. A minimum width above which automatic control was always possible—the automatic lane width—was established to be 2.75 m. Finally, a lane width of 2.72 m was found to have the same probability of automatic and human lateral control, namely the critical lane width. Following a similar methodology, these parameters could be determined for other vehicles, enhancing the interaction between autonomous vehicles and road infrastructure and thus supporting rapid deployment of autonomous technology without compromising safety.

In the last two decades, the demand for geo-spatial data has tremendously increased. Mobile mapping Systems (MMS) are considered the main source of data for mapping and Geographic Information Systems (GIS) applications. MMS, which integrates navigation systems (such as Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS)) and imaging sensors (such as cameras and laser scanners), have been an emerging trend in mapping applications because they allow the implementation of the mapping concepts from moving vehicles (e.g. cars, airplanes and trains). The main strength of MMS which makes it desirable for different mapping applications is their ability to directly georeference their mapping sensors, which means knowing their position and orientation relative to a mapping co-ordinate frame. Once geo-referenced, the mapping sensor can be used to determine the positions of points external to the platform in the same mapping co-ordinate frame. Nevertheless, the major drawback of current MMS systems is their high price due to the type of the used platform, navigation and mapping technologies. Today's smartphones are getting more sophisticated and smarter and therefore they will narrow the gap between computers and mobile phones. Current generation of smartphones are equipped with GNSS receivers, micro-electro mechanical systems (MEMS) based navigation sensors (such as gyroscopes, accelerometers, magnetometers, and barometers) and high resolution digital cameras. As a result, smartphones are now getting quite popular for data collection projects including point data mapping tasks and it is not far before we see smartphones are used as mobile mappers. This paper will present a new and innovative application of a portable MMS based on smartphones that overcome the high-cost drawback of the current land-based MMS with reasonable accuracy.

Driving Speed Behaviour Approaching Road Work Zones On Two-Lane Rural Roads

A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10–0.20 m, and vertical accuracy was approximately 0.01–0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed.

Influence of road markings, lane widths and driver behaviour on proximity and speed of vehicles overtaking cyclists

Evaluation of static three-dimensional (3-D) laser scanning point cloud accuracy has become a topical research issue. Point cloud accuracy is typically estimated by comparing terrestrial laser scanning data related to a finite number of check point coordinates against those obtained by an independent source of higher accuracy. These methods can only estimate the point accuracy but not the point cloud accuracy, which is influenced by the positional error and sampling interval. It is proposed that the point cloud error ellipsoid is favorable for inspecting the point cloud accuracy, which is determined by the individual point error ellipsoid volume. The kernel of this method is the computation of the point cloud error ellipsoid volume and the determination of the functional relationship between the error ellipsoid and accuracy. The proposed point cloud accuracy evaluation method is particularly suited for small sampling intervals when there exists an intersection of two error ellipsoids, and is suited not only for planar but also for nonplanar target surfaces. The performance of the proposed method (PM) is verified using both planar and nonplanar board point clouds. The results demonstrate that the proposed evaluation method significantly outperforms the existing methods when the target surface is nonplanar or there exists an intersection of two error ellipsoids. The PM therefore has the potential for improving the reliability of point cloud digital elevation models and static 3-D laser scanning-based deformation monitoring.

Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.

Abstract. Recently, with the progress of laser scanning technology, it has become possible to scan several environments such as buildings, roads, and cities easily and at a low cost. There are typically three scanning methods: TLS (Terrestrial Laser Scanning), MMS (Mobile Mapping System), and ALS (Airborne Laser Scanning), and the environments are scanned for measurement, modelling, and simulation etc. Therefore, an efficient and comprehensive data management method, which can handle these point clouds in various applications is strongly required. In this paper, we describe a method of data management for efficient search and rendering of multiple large scale point clouds. First, we define a general point cloud file format including the octree and the quantization structure. Next, the data management method enabling an efficient search and rendering of the point clouds is proposed. Finally, the effectiveness of our method is verified using several actual data sets.

Lane marking detection is challenging under adverse weather, especially in rain. Rain affects the visibility by changing the amount of light reflected from the road and splashing of rain periodically blocks vision through the car windshield. Colored (yellow, blue etc.) and eroded lane markings have lower contrast between lane and road surface. Rain affects the visibility of these colored and eroded lanes severely. In this paper, a robust lane detection and tracking method is proposed to detect lane under heavy rainfall condition. Here, an automatic adjustable threshold (AAT) is proposed, which improves the performance of canny edge detector to detect colored, eroded and blurred lane markings. Probabilistic Hough Transform (PHT) is used to detect candidate lane lines. A lane verification technique slope and angle based geometric constraints (SAGC) is used to detect and verify lane markings. Finally, a lane tracking technique by angle range validation (ARV) is introduced, which can keep track of the lane position when either left or right or both lane markings are partially or fully invisible due to erosion or occlusion or when lanes are not even painted on the road. Experimental results show that the average detection rate is 94.12%, and the average detection time is 30.24ms per frame which outperformed state-of-the-art method.