Local map Construction Methods with SD map: A Novel Survey
Jiaqi Li Pingfan Jia Jiaxing Chen Jiaxi Liu Lei He

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Local map Construction Methods with SD map: A Novel Survey
    
    Jiaqi Li^*, Pingfan JiaJiaqi Li is with the Department of Civil Engineering, Tsinghua University, Beijing, 100084, China (e-mail: lijq22@mails.tsinghua.edu.cn). 
    Pingfan Jia is with the School of Computer Science, Beihang University, Beijing, China (e-mail: pingfan@buaa.edu.cn).
    Jiaxing Chen is with the School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China (e-mail: chenjx23@mails.tsinghua.edu.cn). 
    Jiaxi Liu is with the Civil and Environmental Engineering Department, University of Wisconsin-Madison, WI, 53715, USA (e-mail: jliu2487@wisc.edu).
    , Jiaxing Chen, Jiaxi Liu, Lei HeLei He is the corresponding author, with School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China (e-mail: helei2023@mail.tsinghua.edu.cn).

     
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§ ABSTRACT 
 In recent years, significant academic advancements have been made in the field of autonomous vehicles, with Local maps emerging as a crucial component of autonomous driving technology. Local maps not only provide intricate details of road networks but also serve as fundamental inputs for critical tasks such as vehicle localization, navigation, and decision-making. Given the characteristics of SD map (Standard Definition Map), which include low cost, ease of acquisition, and high versatility, perception methods that integrate SD map as prior information have demonstrated significant potential in the field of Local map perception. 
 
 The purpose of this paper is to provide researchers with a comprehensive overview and summary of the latest advancements in the integration of SD map as prior information for Local map perception methods. This review begins by introducing the task definition and general pipeline of local map perception methods that incorporate SD maps as prior information, along with relevant public datasets. And then it focuses on the representation and encoding methods of multi-source information, as well as the methods for fusing multi-source information. In response to this burgeoning trend, this article presents a comprehensive and meticulous overview of the diverse research efforts in this particular field. Finally, the article addresses pertinent issues and future challenges with the aim of guiding researchers in understanding the current trends and methodologies prevalent in the field.





§ INTRODUCTION


Local map perception is a critical and challenging task in the field of intelligent driving. It involves detailed understanding and real-time modeling of the environment surrounding the vehicle, serving as the foundation for decision-making and navigation in autonomous systems. Local maps not only provide information about roads and lanes but also encompass the detection and recognition of obstacles, traffic signs, pedestrians, and other dynamic or static objects. This information is essential for ensuring the safe operation of the vehicle and efficient path planning. Without precise local map perception, autonomous vehicles may deviate from their routes, cause traffic accidents, and potentially endanger passenger safety. Thus, Local map perception plays an indispensable role in the autonomous driving ecosystem.


Unlike typical object detection, Local map perception must handle complex and dynamic environmental information while maintaining high accuracy under various lighting conditions and weather situations. For example, shadows on the road, light reflections, dynamic obstacles, and occlusions of road signs can all interfere with Local map perception.

Additionally, sensor noise and data delays further complicate the perception task. Therefore, developing robust Local map perception technologies is crucial for achieving safe and reliable autonomous driving.
To solve this kind of problem, many researchers have proposed various methods. Chen and Lei <cit.> proposed a method for visual localization and map construction using ground texture, enhancing the accuracy of positioning and the precision of map updates through global and local optimization. <cit.> enhanced online map prediction and lane topology understanding by leveraging SD maps and integrating SD map information through a Transformer encoder, which alleviates problems with obscured lane lines or poor visibility and significantly improves the performance of lane detection and topology prediction. <cit.> proposed an innovative video lane detection algorithm that enhances the feature map of the current frame using an occlusion-aware memory-based refinement (OMR) module, leveraging obstacle masks and memory information to improve the detection accuracy and robustness under occlusion. RVLD<cit.> improved the reliability of lane detection by recursively propagating the state of the current frame to the next frame, utilizing the information from the previous frames. Besides, there are Laneaf<cit.>, LaneATT<cit.>, Streammapnet<cit.> to ease these issues.

In previous autonomous driving research, high-definition maps (HDMap) have been vital. HDMap, characterized by absolute and relative accuracies within 1 meter, provides high precision, freshness, and richness of electronic maps, including extensive road and environmental information. These maps offer precise navigation and positioning services to support safe and efficient autonomous driving. However, HDMap faces significant challenges, primarily in terms of real-time updates and cost control. Urban road environments change frequently, and any minor alteration can impact the driving safety of autonomous vehicles. Traditional HDMap production methods require substantial time and resources, making real-time updates difficult, both <cit.> and <cit.> have pointed out similar issues. Moreover, the production and maintenance costs of HDMap are extremely high, with costs reaching thousands of dollars per kilometer using traditional methods.

In this context, the "highly rely on perception, reducing dependence on maps" approach has gained widespread recognition within the industry. This approach emphasizes the use of onboard sensors for autonomous driving perception tasks, supplemented by lightweight map information. This strategy reduces reliance on real-time map updates, lowering maintenance costs, while lightweight map information can effectively compensate for certain limitations of onboard sensors, enhancing the model's robustness. SD map, as a widely used electronic map in traffic navigation and geographic information services, feature low production and maintenance costs, easy accessibility, and small data size, making it suitable as lightweight maps to assist onboard sensors in constructing local maps for autonomous driving.

Despite the promising prospects and numerous challenges in constructing Local maps based on SD map, there is a lack of comprehensive research reviews in this area. To address this gap, this review aims to provide a thorough overview of the latest advancements in Local map construction methods utilizing SD map. Specifically, the focus is on the application of SD map information representation methods and multimodal data fusion techniques in local map perception tasks. This research delves into the major developments, challenges, and research directions in this field. The contributions to the existing body of knowledge are as follows, The existing literature on local map construction using SD map as a prior is reviewed. The strengths and limitations of these approaches are analyzed, providing insights into their effectiveness and applicability in real-time autonomous driving applications. The representation and encoding methods of information from various sensors, as well as the fusion techniques for multi-source sensor data, are highlighted, which are crucial for real-time local map generation. The underlying principles, architecture, and performance of these methods are discussed, shedding light on their feasibility and practicality in the field key challenges and open research questions in local map construction using SD map as a prior are identified.









§ BACKGROUND



In this section, the definition of Local map Construction with SD map is clarified, and the general pipeline for this type of task is summarized. The composition and application scenarios of SD map are introduced. Finally, commonly used public datasets and evaluation metrics in Local map perception tasks are listed.




 §.§ Task Definition of Local map Construction with SD Map


The task of Local map perception involves creating an accurate map representing the vehicle's surrounding environment to support autonomous driving decision-making and planning. This task typically relies on data from various sensors, including cameras, LiDAR, radar, and GPS. Additionally, incorporating prior information from SD map enhances the model's robustness and mitigates the impact of uncertainties from onboard sensors, improving the overall model performance. The core of the Local map perception task is real-time sensing and understanding of the vehicle's surroundings.

The general process of neural networks used for Local map construction can be summarized into several key components, as illustrated in Fig. <ref>. After inputting surround view images and LiDAR point clouds, the overall architecture of the Local map construction network can be depicted as consisting of different parts: a backbone for image feature extraction, a PV2BEV (Perspective View to Bird's Eye View) module for perspective transformation, a module for multimodal feature fusion, and task-specific heads for lane detection. These components form the basic framework of the Local map perception network. The images and point cloud data captured by the surround view cameras and LiDAR are first processed by a backbone to obtain (multi-scale) image features. These features are then transformed to the BEV perspective using the PV2BEV module, followed by fusion with SD map data through a modality fusion module, and finally output through different task-specific heads.






 §.§ Standard Definition Map


SD map, short for Standard Definition Map, is a digital map technology providing basic geographic information and road network structures. It is widely used in everyday navigation and geographic information services, offering convenience to users. SD map primarily provides the centerline skeleton of roads without detailed lane information, road signs, or other high-precision environmental features. For the task of Local map construction, SD map offers three main advantages. First, SD map data is easily accessible. It can typically be obtained for free from open geographic data sources such as OpenStreetMap <cit.>, making it suitable for large-scale applications. Second, compared to HDMap, the production and maintenance costs of SD Map are significantly lower. Lastly, SD map has high universality, covering most types of roads, and can provide relevant road information for local map construction tasks. Platforms like OSM and Baidu Maps can serve as data sources for SD map. For instance, OpenStreetMap (OSM) is a collaborative project created and maintained by global volunteers, providing free, editable, and open-content maps. OSM data includes a wide range of geographic information such as roads, buildings, parks, and rivers, which users can freely access, edit, and use.
























 §.§ Datasets


In the field of BEV Local map construction, commonly used datasets include KITTI<cit.>, nuScenes<cit.>, ApolloScape<cit.>, Argoverse<cit.>, Openlane<cit.>, and Waymo<cit.> Open Dataset. 

The KITTI<cit.> dataset, created by the Karlsruhe Institute of Technology and the Toyota Technological Institute, provides stereo camera, LiDAR, and GPS/IMU data, covering urban, rural, and highway scenes, and is suitable for tasks such as object detection, tracking, and road detection. 

nuScenes<cit.>, released by Motional, includes data from six cameras, five radars, one LiDAR, IMU, and GPS, suitable for urban traffic scenarios under various weather and lighting conditions. 

ApolloScape<cit.>, released by Baidu, offers high-precision 3D annotated data covering various urban road scenes, suitable for tasks like lane detection and semantic segmentation. 

Argoverse<cit.>, released by Argo AI, includes stereo camera, LiDAR, GPS, and IMU data, providing detailed 3D annotations and lane markings, primarily used for 3D object detection and lane detection. 

The Waymo<cit.> Open Dataset, released by Waymo, covers a variety of weather and traffic conditions, providing high-quality data from LiDAR and cameras, suitable for tasks such as 3D object detection, tracking, and lane detection.

OpenLane-V2<cit.>, also known as OpenLane-Huawei or Road Genome, is a benchmark dataset for next-generation autonomous driving scene road structure perception, jointly open-sourced by Shanghai Artificial Intelligence Laboratory and Huawei Noah's Ark Laboratory. It is the first dataset to include the topological relationships of road structures in traffic scenes.

ONCE-3DLanes<cit.> dataset, a real-world autonomous driving dataset with lane layout annotation in 3D space,  is a new benchmark constructed to stimulate the development of monocular 3D lane detection methods.  It is collected in various geographical locations  in China, including highway, bridge, tunnel, suburb and downtown, with different weather conditions (sunny/rainy) and lighting conditions (day/night). The whole dataset contains 211K images with its corresponding 3D lane annotations in the camera coordinates.  

CurveLanes<cit.> is a new benchmark lane detection dataset with 150K lanes images for difficult scenarios such as curves and multi-lanes in traffic lane detection. It is collected in real urban and highway scenarios in multiple cities in China. All images are carefully selected so that most of them image contains at least one curve lane. More difficult scenarios such as S-curves, Y-lanes, night and multi-lanes can be found in this dataset.




 §.§ Common Evaluation Metrics




  §.§.§ Metrics for Lane Extraction


Mean Average Precision (mAP) is a common metric used to evaluate the performance of object detection models. mAP measures the precision of a model at various threshold levels by matching predicted bounding boxes with ground truth boxes to calculate true positives (TP), false positives (FP), and false negatives (FN). Initially, predicted boxes are matched with ground truth boxes based on a specified IoU (Intersection over Union) threshold. Then, precision (TP / (TP + FP)) and recall (TP / (TP + FN)) are calculated for each class and used to plot the Precision-Recall curve. The area under this curve is calculated using interpolation methods to obtain the Average Precision (AP) for a single class. Finally, the mean of the AP values across all classes gives the mAP, reflecting the overall detection performance of the model, with higher values indicating better performance.

    mAP = 1/N∑_i=1^NAP_i


Mean Intersection over Union (mIoU) is a commonly used metric to evaluate the performance of semantic segmentation models. mIoU measures the classification accuracy of the model at the pixel level for various objects. The calculation involves several steps. For each class, the IoU is computed by dividing the number of intersecting pixels (Intersection) between the predicted and ground truth areas by the union of these areas (Union).  This calculation is performed for each class, and the mean IoU across all classes gives the mIoU, providing an average performance evaluation of the model's segmentation accuracy, with higher values indicating better segmentation performance.

    mIoU = 1/C∑_c=1^CTP_c/FP_c + FN_c + TP_c


Traditional object detection metrics like mAP may not fully capture all important aspects of the detection task, such as the estimation of object speed and attributes, and the accuracy of position, size, and orientation. Therefore, the nuScenes<cit.> Detection Score (NDS) has been proposed to comprehensively account for these factors. NDS integrates multiple key metrics to overcome the limitations of existing metrics and provide a more holistic performance evaluation.

The NDS calculation formula is as follows:

    NDS = 1/2× (mAP + mATE)


In this formula, mAP represents the mean Average Precision, measuring detection accuracy. The TP set contains the average values of five True Positive Metrics: ATE (Average Translation Error), ASE (Average Scale Error), AOE (Average Orientation Error), AVE (Average Velocity Error), and AAE (Average Attribute Error).



  §.§.§ Metrics for Topology Reasoning


 OpenLane-V2<cit.> breaks down the task into three subtasks: 3D lane detection, traffic element recognition, and topology reasoning. The overall task performance is described using the OpenLane-V2 Score (OLS), which is the average of the metrics for each subtask. The metric for 3D lane detection, DETl, can be expressed as the mean AP at different thresholds t∈ T, T={1.0,2.0,3.0} [Equation], where AP is calculated using the Fréchet distance. Traffic element detection is evaluated similarly to object detection using AP, with an IoU threshold set to 0.75. Traffic elements have various attributes, such as traffic light colors, which are closely related to lane accessibility, so attributes must also be considered. Assuming A is the set of all attributes, the evaluation includes attribute classification accuracy in.

    OLS = 1/4[ DET_l + DET_t + f(TOP_ll) + f(TOP_lt) ]


    DET_l = 1/|T|∑_t ∈ TAP_t


    DET_t = 1/|A|∑_a ∈ AAP_a


OpenLane-V2 uses the TOP score to evaluate the quality of topology reasoning, akin to the mAP metric but adapted for graphs. Essentially, this converts the topology prediction problem into a link prediction problem and calculates mAP (mean APs of all vertices) to assess algorithm performance. The first step is to determine a matching method to pair ground truth and predicted vertices (i.e., centerlines and traffic elements). For centerlines, Fréchet distance is used; for traffic elements, IoU is used. When the confidence score of an edge between two vertices exceeds 0.5, they are considered connected. The vertex AP is obtained by ranking all predicted edges of a vertex and calculating the mean of cumulative precision:

    TOP = mAP = 1/|V|∑_v ∈ V∑_n̂∈N̂(v) P(n̂)1(n̂∈ N(v) )/|N(v)|




§ MULTIMODAL REPRESENTATION







 §.§ Image Data

In the perception task of BEV, the image information of the panoramic camera is the most important input data, and the common feature extraction method of the panoramic image follows the paradigm of autonomous driving perception task BEVformer<cit.> or LSS<cit.>. The Backbone module of neural networks extracts 2D image features from various camera perspectives through classic and lightweight convolutional networks such as ResNet-50<cit.> or 101, Mobilenets<cit.>, EfficientNet<cit.>, V2-99<cit.> and so on. Among them, the ResNet series is widely used because of ResNet architecture addresses the vanishing gradient problem in deep neural networks during training by introducing residual blocks. Variants like ResNet enhance feature extraction capabilities by increasing network depth and width. These networks are extensively utilized in BEV Local map perception tasks due to their outstanding performance in image recognition and feature extraction. Typically,  a Feature Pyramid Network (FPN)<cit.> module is appended to the Backbone module. The FPN integrates feature maps of different scales, generating more robust multi-scale feature representations. This seems to be the default basic configuration, and the number of fusion layers can be selected according to the network type. This multi-scale feature fusion aids in improving the detection and recognition of objects of varying sizes, thereby enhancing overall performance.

In addition to these lightweight and simple backbone, larger backbone networks will be the mainstream trend in the future. With the success of Transformers in the field of computer vision<cit.>, Transformer-based feature extraction methods have also been applied to BEV Local map perception tasks such as Swin<cit.>. Refer to the methods on the Nuscece leaderboard, The state-of-the-art methods all use pre-trained VIT-L as the backbone network, or its variant EVA-02<cit.>. This large pre-trained backbone network is the key to improving model performance, although the large number of parameters and computational complexity of large models can seriously affect inference speed. Nevertheless, its performance directly promotes detection accuracy.
The training of these large models requires massive data support, but data labeling is costly and limited, and self supervised training methods will become mainstream. With the widespread application of BERT<cit.> pre-trained models in various downstream tasks for self supervised tasks in natural language processing, it has demonstrated a powerful ability to learn language representations. Similarly, in self supervised learning in computer vision tasks, MAE<cit.> randomly masks patches on images and implements self supervised learning of masked images. The achievements of MIM<cit.> based pre-training algorithms are flourishing in the field of computer vision. This self supervised pre-training model can not only solve the problem of high cost labels, but also better learn the representation relationship of images.

Whether based on CNN or Transformer methods, the ultimate goal is to obtain high-quality feature representations of panoramic images. For BEV Local map perception tasks, feature representation is crucial as it directly affects the accuracy and robustness of the perception system. The global feature extraction mechanism of FPN module or Transformer can significantly improve the overall performance of the network, making it more effective in perception and decision-making in complex driving environments.



 §.§ Lidar Points Data

In the Local map perception task of BEV, in addition to using a pure visual surround camera as a single data input, multimodal methods fuse multi-modal information such as lidar point clouds and camera data to perform depth aware BEV transformation. Compared to single vision and multimodal (RGB+LiDAR) methods, multimodal fusion methods perform excellently in accuracy despite increasing additional computational complexity.
The processing of lidar point cloud data is a crucial step in multimodal perception tasks. The feature extraction of lidar point cloud data in P-mapnet<cit.> requires voxelization of the point cloud first, followed by the use of multi-layer perceptron (MLP) to extract local features of each point. Maximizing pooling selects the largest feature value from multiple local features to form a global feature representation, enhancing the model's global perception ability of point cloud data<cit.>.

Given the LiDAR point cloud P and panoramic images I.

    M = F_2(F_1(P,I)),


where 𝐹_1 represents the feature extractor, extracting multimodal inputs to obtain BEV features, 𝐹_2 represents the decoder, and outputs the detection results.


The method in MapLite 2.0<cit.> further integrates lidar point cloud data with data from other sensors and integrates it with coarse road maps obtained from SD maps (such as OpenStreetMap) Use the coarse route map information from the SD map to refine the geometric shape and topological structure of the road. This not only improves the accuracy of the map, but also enhances the understanding of complex road environments <cit.>. It is also used to generate high-definition maps online by projecting LiDAR intensity data in bird's-eye view. By integrating multimodal data, not only does it provide detailed spatial information, but it also enables precise semantic segmentation of the driving environment.



 §.§ SD Map Data

In the context of enhancing Local map perception tasks, incorporating SD map information as prior knowledge can significantly improve the performance of vision and lidar sensors, particularly in long-distance and occlusion scenarios <cit.>. To integrate SD maps effectively into network structures while preserving their unique road information, various representations have been explored. SD maps can generally be categorized into two forms: raster and vector.

An example of an SD map is illustrated in Fig. <ref>. This figure demonstrates how different forms of SD map representations can be utilized to supplement the local map construction process, thereby enhancing the overall performance of perception systems.

    M = F_2(F_1(P,L,S)),


Feature extractors can contain multiple modal data. Here S is the SD map prior that comes in the form of road center-line skeletons. Where 𝐹_1 represents the feature extractor, extracting multimodal inputs to obtain BEV features, 𝐹_2 represents the decoder, and outputs the detection results.







  §.§.§ Representation of Raster

MapLite2.0 <cit.> was the first to introduce SD map into Local map perception tasks. PriorLane <cit.> modeling the map as a binary image, where 1 represents the drivable area and 0 represents the non drivable area. Similarly, MapVision<cit.> also uses the one hot encoding method, then concatenates the position encoding information and extracts the SD map features through the encoder. The SD map is aligned with ego data through the KEA module proposed in the article, and then fused with sensor data to obtain a mixed expression. Both P-MapNet and MapLite2.0 use rasterization to represent SD map, but the difference is that after P-MapNet<cit.>, a CNN network is used to extract information from the rasterized SD map, which is used as a source of additional information for BEV feature optimization (i.e. key and val); MapLite2.0 takes SD map as the initial estimate of HDMap, converts it into a BEV perspective and combines it with images input from sensors. It is trained through a Convolutional Neural Network to predict its semantic labels. Finally, these semantic segmentation results are transformed into distance transformations for specific labels, and a structured estimator is used to maintain Local map estimates and integrate SD map priors.




  §.§.§ Representation of Vectors


SMERF<cit.> was the first to propose a Transformer based encoder model for road topology inference. MapEX<cit.> and SMERF have similar representations of map elements, introducing a ployline sequence representation and a Transformer encoder to obtain the final map representation of the scene. Specifically, the roads in SD map are first abstracted in the form of polylines. For the ployline data, N data points are obtained through uniform sampling. Then, after sin cos encoding, the N-d dimensional line description is obtained. Consider a vertical line with small curvature, which is characterized by very similar x or y axis values for all points. Directly inputting the coordinates of these points into the model may result in insufficient differentiation of this curvature. 

Therefore, using sine embedding will make this difference more apparent, thereby improving the interpretability of the model for these features. In practice, the coordinates of each line will be normalized to the range of (0,2π) relative to the BEV range before embedding the coordinates of each line. These encoded data will go through several layers of transformers to obtain map feature representations.


  §.§.§ Encoding of Other Information


SMERF: In addition to encoding the polyline coordinates of SD map. SMERF use one hot encoding to encode the type of road into a vector with dimension K (the number of road types). For ground elements within the perceptual range, M * (N * d + K) encoded data will be obtained, which will be transformed through several layers to obtain map feature representations. The ablation experiment has shown that adding more road type information can improve the effectiveness of lane detection and road topology inference.



§ MULTIMODAL FUSION METHOD

 The method of using only images as input, exemplified by MapTR <cit.>, based on the encoder-decoder architecture, has established a classic paradigm for local map construction, paving the way for subsequent approaches. Streammapnet <cit.> further enhances this by incorporating comprehensive temporal information, significantly improving performance in occluded areas.
3D LaneNet <cit.> adopts an end-to-end learning framework, integrating tasks such as image encoding, spatial transformation between image views and top-down views, and 3D curve extraction into a single network. Gen LaneNet <cit.> proposes a two-stage framework that decouples the learning of image segmentation sub-networks and geometric encoding sub-networks. 

Additionally, several monocular 3D lane detection methods, such as <cit.>, <cit.>, and <cit.>, focus solely on visual images as input.
Numerous models, including <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, and <cit.>, also rely solely on visual images. 
On the other hand, HDMapNet <cit.>, a representative multimodal method, integrates LiDAR point clouds by encoding these features and predicting vectorized map elements in bird's-eye views, achieving effective fusion of multi-sensor data. Furthermore, other models, such as <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, <cit.>, and <cit.>, incorporate LiDAR point cloud data as additional input. Fig. <ref> illustrates the development trends in local map construction over recent years. Considering the cost of constructing high-precision maps, Maplite 2.0 <cit.> was the first to introduce SD maps into local map perception tasks.
MapEX <cit.> addresses situations where existing map information is incomplete or inaccurate by converting existing map elements into non-learnable queries and combining them with learnable queries for training and prediction. SMERF <cit.> and P-MapNet <cit.> combine the feature representation of SD maps with camera input features using a multi-head cross-attention mechanism, enabling more effective lane topology inference.

To achieve effective fusion of visual BEV features and SD map semantic information, BLOS-BEV <cit.> has explored various feature fusion methods. Additionally, methods such as PriorLane <cit.>, FlexMap <cit.>, Bayesian <cit.>, TopoLogic <cit.>, LGMap <cit.>, MapVision <cit.>, RoadPainter <cit.>, and EORN <cit.> integrate SD map priors to support local map construction, a trend that is gradually gaining traction.

A perspective transition is required before fusion. The focus of this section is to convert feature information extracted from 2D camera sensor images (commonly referred to as perspective views (PV)) into BEV features. Local map perception tasks typically view the ground as a plane, in the Bird's Eye View, Establishing a map in BEV, because on one hand, BEV facilitates information fusion of multiple sensors, and existing advanced BEV object detection work can provide a good foundation. There are both geometric based and network-based methods for converting the perspective from PV to BEV. Geometric methods can be divided into those based on isomorphic transformations and those based on depth estimation. Network based methods can be divided into MLP based and Transformer based methods. The conversion from PV to BEV based on Transformer can usually be directly achieved using the BEV perception model. MapTR<cit.> in Fig. <ref> proposes an optimized GTK module based on the View Transformer module in BEVFormer.


 §.§ Align

Due to the inherent errors in GPS signals and the influence of vehicle movement, both vectorized and rasterized SD map priors inevitably have spatial misalignment with the current BEV space, making it difficult to fully align the two. Therefore, before fusion, it is necessary to spatially align the SD map prior with the current BEV operating space. FlexMap<cit.> uses SLAM trajectory and corrected RTK trajectory to calculate offset and achieve spatial alignment. To solve this problem, PriorMap<cit.> sets up a KEA (Knowledge Embedding Alignment) module to embed SD map prior knowledge and align it with image features in space. Specifically, first, a feature extraction network is used to extract feature points from the image and feature points from SD map prior knowledge.


Subsequently, these feature points are spatially matched using an alignment algorithm based on attention mechanism. Finally, the aligned feature points are further processed through a fusion transformer network. Enhanced the accuracy and robustness of Local map perception algorithms. Similarly, P-MapNet<cit.> first downsamples the rasterized SD map prior, and then introduces a multi head cross attention module that allows the network to use cross attention to determine the most suitable alignment position, effectively enhancing BEV features using SD map prior. As Fig. <ref>, P-MapNet's ablation experiment shows that even in the case of weak alignment with BEV space, directly concatenating SD map prior information still improves the performance of the model. On this basis, adding CNN modules and multi head cross attention modules can further improve the performance of the model. This demonstrates the important role of SD map prior information in Local map perception tasks, even without strict alignment, simply adding rasterized SD map priors can improve model performance.




 §.§ Fusion

After obtaining multi-sensor data feature representations, fusion processing is required to obtain stronger feature representations.

In order to align the features of different sensors, it is necessary to achieve fusion in BEV level features. The image BEV features are obtained from the look around image through the perspective conversion module. In SMERF, the SD map feature interacts with the BEV feature through cross attention mechanism. Firstly, the BEV feature is encoded into a query vector and initialized through self attention mechanism. Given the SD map of the scene, LGMap, As shown in Fig. <ref>  evenly sample along each of the polylines for a fixed number of points. With sinusoidal embedding, BEVFormer apply cross-attention between the SD map feature representation with features from vision inputs on each encoder layer. The SD map features are encoded into key and value vectors, which are then computed through Cross Attention to obtain the final fused camera, BEV features of SD map.
 In addition to the common fusion methods of attention mechanisms, BLOS-BEV shows in Fig. <ref>, explore different fusion schemes that combine visual BEV features and SD map semantics to achieve optimal representation and performance, three SD map fusion techniques were explored: addition, concatenation, and cross-attention. Although all fusion methods outperform those that do not use SD maps, cross attention fusion of SD maps performs the best on the nuScenes and Argorse datasets, demonstrating excellent generalization performance and outstanding performance over long distances (150-200m).


In P-mapnet,  point cloud information has been added, and the lidar point cloud has been voxelated and MLP processed to obtain the feature representation of each point, resulting in Lidar BEV. Fusion of Image BEV and Lidar BEV is used to obtain further fused BEV features. Further convolutional downsampling of the fused BEV features can alleviate the misalignment problem between image BEV features and LiDAR BEV features.




Then, through cross attention mechanism, the SD map good feature interacts with the fused BEV feature, resulting in the final fusion of camera and lidar point cloud, BEV features of SD map. Similarly, MapVision and MapEX, as Fig. <ref> and Fig. <ref> used the SD map feature as the key and value, and the feature map formed from multi-perspective images as the query to perform cross-attention.




To address these issues  such as occlusion and limited sensing range can result in inaccuracies,  RoadPainter present a novel SD map interaction module that effectively augments BEV features by incorporating beyond-visual-range in Fig. <ref>.

EORN in Fig. <ref> rasterize the SD map and generate a SD map in BEV. An SD encoder based on a ResNet-18  to extract SD map features. SD map feature  is then interpolated and concatenated with the BEV feature from images BEV along the channel dimension. Fusion methods uses a simple two-layer convolutional neural network, the ConvFuser, which fuses concatenated features and outputs the fused BEV features. Another method involves a graph-based encoder that fuses SD map graphs with BEV features and combines these with the outputs from the centerline deformable decoder using a multi-head attention mechanism.

The subsequent decoder can calculate and output corresponding results for different tasks through query queries from BEV features containing rich information.









§ CONCLUSION AND DISCUSSIONS

This section explores the challenges of Local maps construction with SD map and points out the future direction. Lastly, this research make conclusions.



 §.§ Challenges and Future Prospects

Enhancements in SD map Encoding and Processing Methods. Proper encoding and processing methods are crucial for leveraging SD map prior information in Local map perception tasks. Current studies employ relatively simple encoding and processing methods for SD map information, whether using raster or vector representations. Future research could explore more efficient encoding and feature extraction methods.
    
Improvements in Aligning SD map Prior Information with BEV Space. Due to the accuracy limitations of GPS sensors, it is challenging to perfectly align SD map prior information with the current BEV operational space. This spatial misalignment can affect the model's detection accuracy to some extent. Enhancing spatial alignment methods can further improve model performance. Future research could consider incorporating temporal information to enhance the alignment accuracy between SD map prior information and BEV space.

Inference of Road Topological Relationships. The topological relationships in Local maps can be divided into two branches: the topological relationships between roads (primarily representing road connectivity) and the topological relationships between roads and traffic signs (including traffic control signals and other directional signs). Enhancing scene understanding of the road environment is crucial for high-level autonomous driving tasks. The OpenLane-v2 dataset is the first public dataset providing topological relationships between roads and between roads and traffic signs. Current research focusing on this area is still limited. Future work could model the topological structures of road networks and the scene understanding tasks of traffic signs using graph neural network models.

Incorporating More SD map Prior Information. Existing research has demonstrated that incorporating more road type information can enhance model performance. However, beyond the basic road network positions and road types, SD map can provide richer prior information. For example, OpenStreetMap offers additional information such as the number of lanes, lane directions, and road topological relationships. Future research could attempt to integrate this diverse information as SD map priors to further enhance the robustness and accuracy of Local map perception models.



 §.§ Conclusions


In this article, the literature on local map construction using SD maps was reviewed, highlighting the pivotal role of SD maps in this task. The definition and core aspects of local map construction with SD maps were presented, demonstrating their significance in developing accurate and reliable maps. Commonly used public datasets and their corresponding evaluation metrics were enumerated.

The main processes of leading technological approaches were summarized, focusing on the representation and encoding methods for data from various sensors, such as LiDAR, cameras, and radar. Advanced fusion techniques for integrating multi-source sensor data,  fusion methods, were explored, along with their respective strengths and limitations.

The evaluation prospects and design trends of local map construction models were discussed. This included addressing emerging challenges, such as improving SD map alignment with BEV perspectives and enhancing encoding and processing methods. The potential of incorporating detailed SD map prior information to model road topological relationships was considered, with the goal of improving scene understanding and supporting higher-level autonomous driving tasks.

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Jiaqi Li received the B.S. degree in Surveying and Mapping Engineering from the Qinghai University, Xining, China, in 2022. He is currently pursuing a M.S. degree at the Department of Civil Engineering, Tsinghua University, Beijing, China. Li’s research interests include computer vision and online HDMapping.




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Pingfan Jia received his M.S. degree from the School of Computer Science at Beihang University in 2022. He is currently working at the Multimodal Perception and Computing Research Lab, where he is engaged in cutting-edge Computer Vision research. His research interests are focused on Autonomous Driving Perception and 3D Semantic Scene Completion.





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Jiaxing Chen received an M.S. degree in Electrical and Computer Engineering from the University of Illinois, Chicago, USA, in 2021 and worked as an Algorithm engineer at the National Innovation Center of Intelligent and Connected Vehicles, Beijing, China from 2021 to 2022. He is currently pursuing a Ph.D. degree at the School of Vehicle and Mobility, Tsinghua University, Beijing, China. Chen’s research interests include computer vision and perception based on Vehicle-Road-Cloud architecture.



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Jiaxi Liu received a B.E. and an M.E. degree in Mechanical Engineering at the School of Vehicle and Mobility, Tsinghua University. He is now pursuing a Ph.D. degree in the Civil and Environment Engineering Department, at the University of Wisconsin-Madison. His research interests include collaborative perception, real-time perception, vehicle-road-cloud integration systems, and LLM-assist autonomous driving.





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Lei He received his B.S. in Beijing University of Aeronautics and Astronautics, China, in 2013, and the Ph.D. in the National Laboratory of Pattern Recognition, Chinese Academy of Sciences, in 2018. From then to 2021, Dr. He served as a postdoctoral fellow in the Department of Automation, Tsinghua University, Beijing, China. He worked as the research leader of the Autonomous Driving algorithm at Baidu and NIO from 2018 to 2023. He is a Research Scientist in automotive engineering with Tsinghua University. His research interests include Perception, SLAM, Planning, and Control.









