MapLite 2.0: Online HD Map Inference Using a Prior SD Map

Review and challenge: High definition map technology for intelligent connected vehicle

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

Smart cities are emerging and under development worldwide. As one of the core components for achieving the goal of smart transportation, autonomous vehicles are undoubtedly on the blueprint for smart cities. The development of autonomous driving cars leads to the demand for highly accurate and detailed machine-readable maps. To meet this demand, the high-definition (HD) maps are being developed. The HD maps are extended three-dimensional maps that tend to contain enough detailed information in the driving environment to be consumed by machines. Both commercial companies and research communities got a recent interest in building and are exploring research problems on this kind of mapping data. Several companies have already defined their commercial HD map formats, but there are arguments that the existing HD map formats are not research-friendly enough. In this paper, we propose to define a research-friendly HD map data model by extending the popular node-edge model that is widely used by researchers. Our model considers both on-road and off-road data to be extensible for future use cases and information that will be available on various three-dimensional objects in the outdoor space. This is partially motivated by several use cases that will benefit from HD mapping data other than the self-driving technology. We give a brief discussion for such potential use cases and how our model will fit their requirements. We also discuss that our model can be still used for human-machine interactions for compatibility with existing techniques and applications.

High Definition (HD) maps are necessary for many applications of automated driving (AD), but their manual creation and maintenance is very costly. Vehicle fleet data from series production vehicles can be used to automatically generate HD maps, but the data is often incomplete and noisy. We propose a system for the generation of HD maps from vehicle fleet data, which is tolerant to missing or misclassified detections and can handle drives with multiple routes, generating a single complete map, model-free and without prior reference lines. Using randomly selected drives as pivot drives, a step-wise lateral sampling of detections is performed. These sampled points are then clustered and aligned using Expectation Maximization (EM), estimating a lateral offset for each drive to compensate localization errors. The clustered points are replaced with the maxima of their probability density function (PDF) and connected to form polylines using a modified rectangular linear assignment algorithm. The data from vehicles on varying routes is then fused into a hierarchical singular map graph. The proposed approach achieves an average accuracy below 0.5 meters compared to a hand annotated ground truth map, as well as correctly resolving lane splits and merges, proving the feasibility of the use of vehicle fleet data for the generation of highway HD maps.

Autonomous vehicle technology has been advancing over the past decade with many challenges and obstacles. One obstacle to achieving fully autonomous driving is the feasibility of efficiently producing robust high definition (HD) maps of existing road infrastructure in a sustainable manner. HD maps are digital twins of road infrastructure that include 3-D representation of road features critical to a vehicle’s ability to navigate a road and maintain a consistent trajectory. This includes lane markings, barriers, and road edges. HD maps are often produced by first surveying roads using remote sensing technology. The collected data are then segmented using computer vision and machine learning algorithms to produce an HD map of the features of concern. One common challenge that is specific to extracting lane marking information is that the markings are occasionally occluded or degraded by high traffic volumes. This results in failure to detect lane markings and the existence of significant gaps in the extracted information. These gaps are manually filled in by quality control staff in a tedious and time-consuming process. To help overcome such challenges, this paper proposes a novel algorithm through which Kalman filtering is combined with Bézier curve fitting to automatically refine lane marking information. The proposed algorithm is tested on multiple roads where mobile lidar data were collected and segmentation was first applied using deep learning technology. The proposed algorithm was then used to refine the lane marking information which helped close all the gaps and recreate missing portions of the extracted lane markings.

State estimation plays an important role in most navigation tasks for mobile robots. In the case of autonomous vehicles, localization is primarily performed using measurements from Global Navigation Satellite System (GNSS) devices, high-definition (HD) maps, or both. On the other hand, aerial and derived maps, or higher-level maps, such as OpenStreetMap or Google Maps, represent more financially viable alternatives and, depending on how they are used, with more stable characteristics. However, the task of associating low-level sensor measurements with high-level features of these maps tends to be more complex given that the data representation domains tend to be different. Therefore, in this thesis, we propose to combine techniques to associate low-level sensor data with a more abstract map for localization in the context of intelligent vehicles. Among the results obtained with the last of the proposed methods, we achieved a localization with an average error of 3:4m using the trajectories from the Kitti dataset, surpassing the popular method proposed in (MILLER et al., 2021) by more than 1:7m on the same dataset. Finally, the methods proposed in this thesis aimed at modularization and integration with more complex systems, so that - even without the intention of merging with HD or GNSS maps - these can be integrated into the same system.

High-Definition Maps (HD maps) are essential for the precise navigation and decision-making of autonomous vehicles, yet their creation and upkeep present significant cost and timeliness challenges. The online construction of HD maps using on-board sensors has emerged as a promising solution; however, these methods can be impeded by incomplete data due to occlusions and inclement weather, while their performance in distant regions remains unsatisfying. This paper proposes PriorDrive to address these limitations by directly harnessing the power of various vectorized prior maps, significantly enhancing the robustness and accuracy of online HD map construction. Our approach integrates a variety of prior maps uniformly, such as OpenStreetMap's Standard Definition Maps (SD maps), outdated HD maps from vendors, and locally constructed maps from historical vehicle data. To effectively integrate such prior information into online mapping models, we introduce a Hybrid Prior Representation (HPQuery) that standardizes the representation of diverse map elements. We further propose a Unified Vector Encoder (UVE), which employs fused prior embedding and a dual encoding mechanism to encode vector data. To improve the UVE's generalizability and performance, we propose a segment-level and point-level pre-training strategy that enables the UVE to learn the prior distribution of vector data. Through extensive testing on the nuScenes, Argoverse 2 and OpenLane-V2, we demonstrate that PriorDrive is highly compatible with various online mapping models and substantially improves map prediction capabilities. The integration of prior maps through PriorDrive offers a robust solution to the challenges of single-perception data, paving the way for more reliable autonomous driving.

In recent years, high-definition (HD) maps are considered to be significant complements to the on-board sensors of autonomous vehicles. Due to the highly dynamic characteristic of HD map, how to update HD maps fast and efficiently has become the pivotal issue which attracts great attentions. Currently, most of the research on this issue focuses on the crowdsourcing protocol design or specific updating method meanwhile disregarding the need of communication and computing resource in HD map update. Therefore, in this paper, we propose an integrated update framework including both an adaptive data transmission scheme and an adaptive task scheduling scheme to realize fast data collection and fast data processing for HD map update. To the best of our knowledge, this is the first work that addresses the problem of HD map update which takes the need of communication and computing resource into consideration. In this paper, we firstly illustrate the system architecture of our HD map update framework. Based on that, to realize fast data collection, we propose an adaptive data transmission scheme, which optimizes the transmission rate on different links of each vehicle considering the wireless channel capacity and fronthaul link capacity of the access points. Besides, to realize fast data processing, we propose an adaptive task scheduling scheme, which enables cooperative computing between the edge nodes and cloud computing server, considering the constraints of computing capacity and backhaul link capacity. To the end, we carry out simulations to verify the effectiveness of our proposed policies.

High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environment-Aware Normal Distributions Transform (EA-NDT), that significantly improves compression of standard NDT map representation. The compressed representation of EA-NDT is based on semantic-aided clustering of point clouds resulting in more optimal cells compared to grid cells of standard NDT. To evaluate EA-NDT, we present an open-source implementation that extracts planar and cylindrical primitive features from a point cloud and further divides them into smaller cells to represent the data as an EA-NDT HD map. We collected an open suburban environment dataset and evaluated EA-NDT HD map representation against the standard NDT representation. Compared to the standard NDT, EA-NDT achieved consistently at least 1.5x higher map compression while maintaining the same descriptive capability. Moreover, we showed that EA-NDT is capable of producing maps with significantly higher descriptivity score when using the same number of cells than the standard NDT.

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.

A high-definition (HD) map is becoming an integral component of future mobility systems such as autonomous and connected vehicles. Advances in computing systems, LiDAR technologies, and vehicle communication technologies have enabled the HD map to directly treat a point cloud map (PCM), modeling road environments as LiDAR signal-level data. However, if actual road environments are changed, the PCM, modeling the environments before changes, can not be used for vehicle applications. Accordingly, the PCM has to stay up-to-date states by reflecting the environment changes continuously. This paper presents a crowd-sourcing framework to update the PCM from environment changes continuously using LiDAR and vehicle communication. Multiple intelligent vehicles installed with the LiDAR sensors download the PCM from a map server via wireless vehicle communication. To minimize the effects of environment changes, a robust localization based on a hierarchical Simultaneous Localization and Mapping (SLAM) estimates the pose (position and direction). The estimated pose is used to detect the differences between the PCM and environments, which are defined as map changes. The map changes are detected by the probabilistic and evidential theory considering the LiDAR characteristics, such as beam divergence and multi-echo. The detected map changes are uploaded to the map cloud server and merged into the PCM. The proposed crowd-sourcing framework to keep the PCM up-to-date is verified and evaluated via simulations and experiments in sites with road environment changes.

In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and labeling high-definition (HD) maps accurately and efficiently. Today, the process of creating HD maps requires a lot of human input, which takes time and is prone to errors. In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data. We implemented and tested our methods on several urban scenarios using data collected from our test vehicle. The results show that the proposed deep learning based method can produce highly accurate HD maps. This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicles.

Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively.

High-definition (HD) maps aim to provide detailed road information with centimeter-level accuracy, essential for enabling precise navigation and safe operation of autonomous vehicles (AVs). Traditional offline construction methods involve several complex steps, such as data collection, point cloud generation, and feature extraction, but these methods are resource-intensive and struggle to keep pace with the rapidly changing road environments. In contrast, online HD map construction leverages onboard sensor data to dynamically generate local HD maps, offering a bird’s-eye view (BEV) representation of the surrounding road environment. This approach has the potential to improve adaptability to spatial and temporal changes in road conditions while enhancing cost-efficiency by reducing the dependency on frequent map updates and expensive survey fleets. This survey provides a comprehensive analysis of online HD map construction, including the task background, high-level motivations, research methodology, key advancements, existing challenges, and future trends. We systematically review the latest advancements in three key sub-tasks: map segmentation, map element detection, and lane graph construction, aiming to bridge gaps in the current literature. We also discuss existing challenges and future trends, covering standardized map representation design, multitask learning, and multi-modality fusion, while offering suggestions for potential improvements.

High-Definition (HD) Maps are indispensable components of an autonomous vehicle software stack, containing a precise representation of the static surroundings. Prediction, motion planning and vehicle behavior heavily rely on the accuracy of the HD Map. However, a key problem in the mapping process is that the environment itself changes over time, leading to inconsistencies between the real world and the outdated knowledge in the HD Map. Therefore, validating the HD Map features becomes a deciding factor in the accuracy and safety of a self-driving vehicle. Intuitively, the validation can be done by correlating the map information with the acquired sensor measurements. Although, individual sensors are subject to errors, integrating the measurements from various sensor creates a more accurate representation of the vehicle surroundings which subsequently can be used for a more reliable map validation mechanism. In this paper, we propose such a real-time method to validate the HD map by using as input more accurate estimations provided by sensor fusion. The proposed solution is decomposing the high dimensional map validation problem into multiple independent one-dimensional estimators that are able to provide validity probabilities for every independent map feature, i.e., lines, curbs and lane markings.