High-definition Maps Research Articles

Generation of high definition map for accurate and robust localization

This paper presents a framework for generating high-definition (HD) map, and then achieves accurate and robust localization by virtue of the map. An iterative approximation based method is developed to generate a HD map in Lanelet2 format. A feature association method based on structural consistency and feature similarity is proposed to match the elements of the HD map and the actual detected elements. The feature association results from the HD map are used to correct lateral drift in the light detection and ranging odometry. Finally, some experimental results are presented to verify the reliability and accuracy of autonomous driving localization.

Federated HD Map Updating Through Overlapping Coalition Formation Game

High Definition (HD) maps have become core supporting components for autonomous driving. To date, their updates heavily depend on the vehicle fleets of the map vendors, which cannot scale and timely reflect the highly dynamic environment. To ensure the HD map quality, it is advocated social vehicles should be used. Nevertheless, there are privacy concerns and a lack of incentives for social vehicles to contribute data. In this paper, we leverage federated analytics (FA), a newly developed collaborative data analytics paradigm, where raw data are kept local and only the insights generated from local analytics are sent to a server for aggregation. We present a new Federated Analytics based HD map Updating model (FAUMap) to protect the privacy of social vehicles. To motivate social vehicles to contribute data and improve the HD map quality, we formulate an overlapping coalition formation game, OCFUMap, and develop an algorithm to find feasible coalitions. Simulations show that our approach can improve the quality of the updated HD map by 1.56 times. To study an end-to-end operation of the FAUMap model and OCFUMap game, we present a case of HD map updates of the Powell street in San Francisco using the autonomous driving simulator CarLA.

High-Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives

High-Definition Maps: Comprehensive Survey, Challenges, and Future Perspectives

Applying a semi-automated workflow for digital dynamic maps to support the operation of automated vehicles

Automated vehicles (AVs) promise new opportunities for urban transport and potential benefits to road safety, transport efficiency, travel comfort and emission levels. However, substantial technical hurdles need to be overcome before AVs can operate regularly on urban streets and highways. Besides considerable infrastructure adaptations, one way to enable the operation of AVs are digital dynamic maps (DDM or DD-map), which combine a high-definition map of the physical road scape with dynamic road data obtained from digital sources. Here we present the results of the EU Project SHOW on digital dynamic maps and their relevance for city planning, in particular how cities can benefit from the interplay with map providers. We provide a workflow to set-up and operate a digital dynamic map and discuss how this might benefit transportation systems.

Design of Secure and Lightweight Authentication Scheme for UAV-Enabled Intelligent Transportation Systems Using Blockchain and PUF

Unmanned-aerial-vehicle (UAV)-enabled intelligent transportation system (ITS) is an advanced technology that can provide various services including autonomous driving, real-time creation of high-definition maps, and car sharing. In particular, a UAV-enabled ITS can be realized through the combination of traditional vehicular ad hoc networks (VANETs) and UAVs that can act as flying roadside units (RSUs) at the outskirts and monitor road conditions from predefined locations to spot car accidents and any law violations. Notably, to realize these services, real-time communication between UAVs and RSUs must be guaranteed. However, UAVs have limited computing powers, and if extensive computation is required during communication, the provision of real-time ITS services may be hindered. Furthermore, UAVs and RSUs communicate via public channels that are prone to various attacks, such as replay, impersonation, trace, and session key disclosure attacks. Thus, in this article, a secure and lightweight authentication scheme is proposed for UAVs and RSUs using the blockchain technology. The proposed scheme is analyzed using informal and formal methods including Burrows–Abadi–Nikoogadam (BAN) logic, automated validation of internet security protocols and applications (AVISPA) simulation tool, and real–or–random (RoR) model, and its performance is compared with that of related schemes. The results reveal that the proposed scheme is more efficient and secure as compared to the other competing schemes.

Development of a Large-Scale Roadside Facility Detection Model Based on the Mapillary Dataset.

The detection of road facilities or roadside structures is essential for high-definition (HD) maps and intelligent transportation systems (ITSs). With the rapid development of deep-learning algorithms in recent years, deep-learning-based object detection techniques have provided more accurate and efficient performance, and have become an essential tool for HD map reconstruction and advanced driver-assistance systems (ADASs). Therefore, the performance evaluation and comparison of the latest deep-learning algorithms in this field is indispensable. However, most existing works in this area limit their focus to the detection of individual targets, such as vehicles or pedestrians and traffic signs, from driving view images. In this study, we present a systematic comparison of three recent algorithms for large-scale multi-class road facility detection, namely Mask R-CNN, YOLOx, and YOLOv7, on the Mapillary dataset. The experimental results are evaluated according to the recall, precision, mean F1-score and computational consumption. YOLOv7 outperforms the other two networks in road facility detection, with a precision and recall of 87.57% and 72.60%, respectively. Furthermore, we test the model performance on our custom dataset obtained from the Japanese road environment. The results demonstrate that models trained on the Mapillary dataset exhibit sufficient generalization ability. The comparison presented in this study aids in understanding the strengths and limitations of the latest networks in multiclass object detection on large-scale street-level datasets.

Map-enhanced generative adversarial trajectory prediction method for automated vehicles

Trajectory prediction in dynamic and highly interactive scenarios is a critical method for achieving advanced autonomous driving. Maximizing the guidance and constraints provided by high-definition (HD) maps can help improve prediction performance across the board. In this paper, we propose a map-enhanced generative adversarial network (ME-GAN) for vehicle trajectory prediction. The vehicle motion features, map constraints, traffic flow density, and vehicle interactions are comprehensively considered in the generator, and a graph query mechanism is proposed to realize the reuse of the global map. In the discriminator, in addition to considering the authenticity of a generated trajectory and whether it is consistent with the historical trajectory, additional map information is introduced to establish a matching model between the generated trajectory and the current map. Experiments based on the Argoverse and nuScenes dataset are subsequently performed. The experimental results show that our prediction method outperforms state-of-the-art prediction systems, namely, TNT, PRIME and P2T. The strong coupling of the HD map significantly improves the reasonableness of the predicted trajectory.

EXTRACTION OF ROAD MARKINGS FROM MLS DATA: A REVIEW

Abstract. Nowadays, Mobile Laser Scanning (MLS) systems are more and more used to realize extended topographic surveys of roads. Most of them provide for each measured point an attribute corresponding to a return signal strength, so called intensity value. This value enables to easily understand uncolored MLS as it helps to differentiate materials based on their albedo. In a road context, this intensity information allows to distinguish, among others, the main subject of this paper, i.e. road markings. However, this task is challenging. Road marking detection from dense MLS point cloud is widely studied by the research community. It might concern road management and diagnosis, intelligent traffic systems, high-definition maps, location and navigation services. Dense MLS point clouds provided by surveyors are not processed online, they are thus not directly applicable to autonomous driving, but those dense and precise data can be for instance used for the generation of HD reference maps. This paper presents a review of the different processing chains published in the literature. It underlines their contributions and highlights their potential limitations. Finally, a discussion and some suggestions of improvement are given.

CAOM: Change-aware online 3D mapping with heterogeneous multi-beam and push-broom LiDAR point clouds

A High-Definition (HD) map is an essential component not only for autonomous vehicles but also for surveyors in favor of city management. Moreover, the HD map must be up-to-date by reflecting environmental changes continuously. The development of LiDAR Simultaneous Localization And Mapping (SLAM) techniques has provided an effective way to collect precise point clouds for the construction of HD maps, while traditional methods are time-consuming and labor-intensive on account of the complex street scene. Furthermore, life-long 3D mapping is quite necessary and challenging nowadays. In this paper, we introduce a novel change-aware 3D online mapping framework CAOM,11Code at https://github.com/DCSI2022/CAOM. using point clouds collected by Multi-Beam LiDAR (MBL) and Push-Broom LiDAR (PBL) for rapid city map update and change detection. Concerning the prior map with higher precision, heterogenous fusion is performed to rectify the SLAM drift, based on the View-Traced Clouds (ViTC) derived according to their view in common. Meanwhile, corresponding virtual range images are integrated with distance images to detect the 3D changes between the current map and the historical reference map offline. The detection results are later fused in a probabilistic way along with the homogeneous fusion process where a resilient graph consisting of diverse factors is established to maintain the local and global consistency of the final point cloud map. Compared with 3D-CSTM, the real-world experiments have shown an average 0.5 m and max 1.5 m improvement in the ATE (Absolute Translation Error) of the trajectory generated by our system. Quantitative measurements on the individual point accuracy are also conducted to verify the performance of 3D mapping with up to 16% improvement in comparison with respect to the 3D-CSTM method. The effects of the disparity detection and probabilistic fusion can be revealed from the small objects distinguished in the point cloud map as well as > 95% overall accuracy.

Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction

Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. Several methodologies have been proposed to solve prediction problems under different traffic situations. These works usually combine agent trajectories with either color-coded or vectorized high definition (HD) map as input representations and encode this information for behavior prediction tasks. However, not all the information is relevant in the scene for the forecasting and such irrelevant information may be even distracting to the forecasting in certain situations. Therefore, in this paper, we propose a novel generic representation for various driving environments by taking the advantage of semantics and domain knowledge. Using semantics enables situations to be modeled in a uniform way and applying domain knowledge filters out unrelated elements to target vehicle’s future behaviors. We then propose a general semantic behavior prediction framework to effectively utilize these representations by formulating them into spatial-temporal semantic graphs and reasoning internal relations among these graphs. We theoretically and empirically validate the proposed framework under highly interactive and complex scenarios, demonstrating that our method not only achieves state-of-the-art performance, but also processes desirable zero-shot transferability.

Clothoid-Based Lane-Level High-Definition Maps: Unifying Sensing and Control Models

Autonomous vehicles rely on lane-level high-definition (HD) maps for self-localization and trajectory planning. Current mapping, however, relies on simple line models, while clothoid curves have unexplored potential. Clothoids, well known in road design, are often chosen to model the vehicle trajectory in planning and control systems as they describe the road with higher fidelity. For this reason, we propose two vision-based pipelines for generating lane-level HD maps using clothoid models. The first pipeline performs mapping with known poses, requiring precise real-time kinematics GPS (RTK GPS) measurements; the second copes with noisy localizations, solving the simultaneous localization and mapping (SLAM) problem. Both pipelines rely on a line detection algorithm to identify each line marking and perform a graph-based optimization to estimate the map.

Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments.

Global Navigation Satellite Systems provide autonomous vehicles with precise position information through the process of position augmentation. This paper presents a series of performance tests aimed to compare the position accuracy of augmentation techniques such as classical Differential Global Navigation Satellite System, Real-time Kinematic and Real-time eXtended. The aim is to understand the limitations and choose the best position augmentation technique in order to obtain accurate, trustworthy position estimates of a vehicle in urban environments. The tests are performed in and around the German cities of Wuppertal and Duesseldorf, using a vehicle fitted with the navigation system POS-LV 220, developed by Applanix Corporation. In order to evaluate the real-time performance of position augmentation techniques in a highly challenging environment, a total of four test regions are selected. The four test regions are characterized mainly by uneven terrain with tall buildings around the University of Wuppertal, flat terrain with roads of varying width in the city centre of Wuppertal and Duesseldorf and flat terrain in a tunnel section located in the city of Wuppertal. The performances of the different position augmentation are compared using a Root Mean Square (RMS) error estimate obtained as an output from the Applanix system. Furthermore, a High-Definition map of the environment is used for the purpose of model validation, which justifies the use of RMS error estimate as an evaluation metric for the performance analysis tests. According to the performance tests carried out as per the conditions specified in this paper, the Real-time eXtended (RTX) position augmentation method enables to obtain a more robust position information of the vehicle than Real-time Kinematic (RTK) method, with a typical accuracy of a few centimeter in an urban environment.

High-Definition Map Representation Techniques for Automated Vehicles

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment’s information. Among these efforts is the high-definition (HD) map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD maps for automated vehicles. The current state of autonomous vehicle (AV) mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.

Road-Model-Based Road Boundary Extraction for High Definition Map via LIDAR

High definition (HD) map has become indispensable to autonomous driving systems, but its automated construction is challenging. Since road boundary lines are basic elements of the map, accurate and complete extraction of the lines is one of key to build HD map. We present a method to automatically extract the boundary lines based on the road model using multi-beam LIDAR. First, the road model based on B-spline surfaces is constructed to fit the ground, and the multiRANSAC is proposed to remove the surface points. Second, horizontal distance feature and strong straight constraint are used to extract the road boundary lines. Finally, the lines collected from multiple frames are fused to approximate the curves and complete the boundary information. In the matter, the proposed method extracts the lines on the curved and straight road accurately and completely. We verified the feasibility and robustness of the proposed method on KITTI dataset. The average precision, recall and F1 are 0.9599, 0.9066 and 0.9277 respectively, and the average runtime is 28 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ms/frame$ </tex-math></inline-formula> . Results show that the comprehensive performance of the proposed method outperforms the compared approaches.

Visual Mapping and Localization System Based on Compact Instance-Level Road Markings With Spatial Uncertainty

High-definition (HD) map is crucial for intelligent vehicles to perform high-level localization and navigation. To improve the availability and usability of HD map, it is meaningful to investigate crowd-sourced mapping solutions and low-cost map-aided localization schemes which don't rely on high-end sensors. In this letter, we propose a novel vision-based mapping and localization system, which could generate compact instance-level road maps automatically and provide high-availability map-aided localization. The spatial uncertainties of the map elements are taken into consideration by analyzing the inverse perspective mapping (IPM) model, which enables more flexible map usages in both mapping and localization phases of the system. Besides, a pose graph optimization framework is developed for accurate pose estimation by fusing global positioning (GNSS), local navigation (odometry) and map matching information together. Real-world experiments in urban environment were conducted to validate different phases of the system, including on-vehicle mapping, multi-source map merging and map-aided localization.

A Kamm's Circle-Based Potential Risk Estimation Scheme in the Local Dynamic Map Computation Enhanced by Binary Decision Diagrams.

Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers dynamic information from moving entities on the road. Cooperative intelligent transport systems (C-ITS) are a prominent approach to solving the issue and local dynamic maps (LDMs) are expected to realize the ideal C-ITS. An actual LDM implementation requires a fine database design to be able to update the information to represent potential risks based on future interactions of vehicles. In the present study, we proposed an advanced method for embedding the geographical future occupancy of vehicles into the database by using a binary decision diagram (BDD). In our method, the geographical future occupancy of vehicles was formulated with Kamm’s circle. In computer experiments, sharing BDD-based occupancy data was successfully demonstrated in the ROS-based simulator with the linked list-based BDD. Algebraic operations in exchanged BDDs effectively managed future interactions such as data insertion and timing of collision avoidance in the LDM. This result opened a new door for the realization of the ideal LDM for safety in AVs.

Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments.

High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy.

An Improved RandLa-Net Algorithm Incorporated with NDT for Automatic Classification and Extraction of Raw Point Cloud Data

A high-definition map of the autonomous driving system was built with the target points of interest, which were extracted from a large amount of unordered raw point cloud data obtained by Lidar. In order to better obtain the target points of interest, this paper proposes an improved RandLa-Net algorithm incorporated with NDT registration, which can be used to automatically classify and extract large-scale raw point clouds. First, based on the NDT registration algorithm, the frame-by-frame raw point cloud data were converted into a point cloud global map; then, the RandLa-Net network combined random sampling with a local feature sampler is used to classify discrete points in the point cloud map point by point. Finally, the corresponding point cloud data were extracted for the labels of interest through numpy indexing. Experiments on public datasets senmatic3D and senmatickitti show that the method has excellent accuracy and processing speed for the classification and extraction of large-scale point cloud data acquired by Lidar.

Autonomous driving under V2X environment: state-of-the-art survey and challenges

Abstract As Vehicle-to-Anything (V2X) is viewed as emerging technology, which could improve the mobility and efficiency of urban traffic operations, and enhance the safety of drivers. Supported by the construction of vehicular network facilities, V2X has been applied to various urban traffic management systems. However, due to the limitation of wireless communication, outstanding issues on privacy and security in V2X applications are still urgent. The purpose of our studies is to provide a comprehensive review of V2X-aided autonomous driving. Firstly the standards and protocols on V2X technologies for mobile communication and high-definition map are provided. The research scenarios of V2X-aided automobiles on urban links and highways are discussed. Furthermore, the several challenges of legislation in different countries, privacy issues, and security are discussed. Finally, the research directions for V2X-aided autonomous driving are further given.

Centimeter-level positioning by instantaneous lidar-aided GNSS ambiguity resolution

High-precision vehicle positioning is key to the implementation of modern driving systems in urban environments. Global Navigation Satellite System (GNSS) carrier phase measurements can provide millimeter- to centimeter-level positioning, provided that the integer ambiguities are correctly resolved. Abundant code measurements are often used to facilitate integer ambiguity resolution (IAR); however, they suffer from signal blockage and multipath effects in urban canyons. In this contribution, a light detection and ranging (lidar)-aided instantaneous ambiguity resolution method is proposed. Lidar measurements, in the form of 3D keypoints, are generated by a learning-based point cloud registration method using a pre-built high-definition map and integrated with GNSS observations in a mixed measurement model to produce precise float solutions, which in turn increase the ambiguity success rate. Closed-form expressions of the ambiguity variance matrix and the associated Ambiguity Dilution of Precision (ADOP) are developed to provide a priori evaluation of such lidar-aided ambiguity resolution performance. Both analytical and experimental results show that the proposed method enables successful instantaneous IAR with limited GNSS satellites and frequencies, leading to centimeter-level vehicle positioning.