Give response to the reviewer comment by considering the introduction sections and on your own knowledge and write a clarification sentence in 20 words to use in paper
Reviewer comment:
 In the operation of autonomous vehicles, the presence of corners, ramps, moving vehicles, pedestrians etc. are fundamental elements. Only in a limited number of circumstances may produce critical limitation of the information provided by onboard sensors.
introduction sections:
Abstract: The utilization of high-definition (HD) maps play a crucial role in enhancing the capabilities of autonomous vehicles by predicting traffic behavior beyond their sensor range. Autonomous driving may use dynamic HD maps without sensors to localize and determine a vehicle's surroundings. Point cloud processing is difficult due to a large amount of data, inevitable noise, complexity, unknown circumstances, and occlusions. Currently, methods and algorithms with solid built-in features can manage enormous quantities of Mobile Laser Scanning (MLS) point clouds in unpredictable conditions. The objective of this study is to generate a bird's-eye view (BEV) probabilistic semantic map by utilizing semantic point cloud data for extracting valuable road information like road markings. To facilitate effective decision-making, we developed high-definition (HD) maps using MLS point clouds and employed a Pareto optimal approach based on probability-based multi-objective optimization. With the assistance of this suggested optimization method, we enhanced 3D maps with autonomous rapid data collection, low error rates, and dense point clouds. To validate the proposed model and evaluate the efficiency and accuracy of HD maps, we used Pareto analysis-based management procedures to apply the proposed optimization method under high complexity conditions due to the diversity of road users and traffic levels. This study generated HD maps based on the probability outcomes of object features to reach a satisfactory compromise between different design optimization goals. Quantitative assessments based on road extraction datasets showed that our approach achieves an average accuracy, recall, and F1-scores of 89.16%, 88.74%, and 88.95%, respectively, demonstrating its accuracy and robustness compared to existing methods. 

1.	Introduction
With the increasing demand for accurate and reliable maps in the development of autonomous vehicles, it is crucial to conduct a comprehensive review of the literature to identify key advancements and potential areas for improvement. This research focuses on two fundamental aspects of LiDAR-based mapping: high-definition maps and probabilistic-based multi-objective optimization. By examining existing studies and analyzing the interplay between these elements, this study aims to bridge knowledge gaps, harness synergies, and propose an integrated approach for enhanced map generation. Through this study, a deeper understanding of the complex landscape of autonomous vehicle mapping can be attained, ultimately contributing to the advancement of this transformative technology.
1.1. High-definition maps
HD maps play a vital role in enabling autonomous vehicles to understand the stationary elements present in a scene. Because of the characteristics and structure of the data included in HD maps, a majority of these map data are no longer useful as roads are constructed or managed to change. The characteristics and structure of the maps are like cm-level descriptions and interpretations for transport systems, road markings [1], intersections, stoplights and signs, traffic signals, and sometimes even speed restrictions. HD maps give crucial key details for autonomous cars to comprehend a scene's and an image's deterministic components. Previous approaches in HD map generation have predominantly focused on extracting semantics and attributes from data. The development of an automated system capable of performing this process has the potential to enhance the HD map generation process, reduce labor expenses, and further enhance driving safety [1-4]. As autonomous vehicles heavily rely on high-definition maps, these maps have become indispensable sources of information, providing crucial details about the road environment to ensure secure navigation. However, creating high-definition maps is still a challenging and time-consuming task. In recent years, mobile LiDAR systems have emerged as a promising solution for fast and accurate 3D mapping [2], [5]. They offer the ability to capture a large number of point clouds while driving, resulting in high-density, high-precision maps [2], [6].
It is not easy to retrieve semantic labels for the scene at the cm-level. Conditional Random Fields (CRF) were used in earlier research, like [6], to assign semantic labels. Deep learning's development offers encouraging outcomes for extracting semantic information from visuals. Modern semantic segmentation methods, including those presented by Sengupta et al. (2012) [7] and Zhao et al. (2017) [8]; more accurately produce semantic labels at the pixel level, as given by Chen et al. (2018) [9] and Ye et al. (2021) [10]. Researchers have also investigated semantic maps of the environment; examples can be found in [11,12]. Multi-sensor fusion has been employed in certain research to increase the resilience of their segmentation algorithms. However, these methods either do not clearly map the lane and crosswalk necessary data for HD maps or employ remote sensing data to derive road information. Therefore, it would still be interesting to investigate a detailed semantic map for applications involving urban autonomous vehicles.
HD maps, distinct from conventional 2D maps, are created by leveraging mobile laser scanning (MLS), which is a technology used for capturing 3D point cloud data that boasts an absolute localization precision ranging from 10 to 20 cm [12]. MLS systems gather millions of 3D point cloud data at a driving speed in complex urban environments [13]. These point clouds could show detailed 3D road network topologies [14-17]. Autonomous vehicles (AVs) can benefit significantly from the use of highly precise HD maps, surpassing the capabilities of onboard sensors [13], [18-20]. Commonly, corners, ramps, and obstacles such as moving vehicles, pedestrians, and cyclists can limit the detection range of sensors.
 However, AVs can rely on supplementary information from a predefined HD map, expanding their sensing capabilities beyond the range of onboard sensors. This allows them to anticipate impediments and sudden curves ahead, facilitating rapid decision-making [14], [21]. The integration of real-time sensory detection with prior map data enriches the localization process, transforming sensor-based environmental detection into a simpler map-based operation. Despite the advantages of HD maps in aiding localization services, they still confront various challenges and issues. The MLS system is a great way to get 3D HD maps that are precise and clear. However, processing point clouds is not simple due to their vast data volume, inherent noise, computational complexity, and geometric distortion [1], [22-24]. Techniques such as stereo cameras, sensor fusion, machine learning, radar-based mapping, crowdsourcing, and hybrid mapping have been offered opportunities to enhance HD maps' accuracy, timeliness, and completeness for autonomous vehicles. Integrating these techniques with the proposed LiDAR-based approach could potentially lead to more comprehensive and robust HD map generation methods. In addition to mobile LiDAR systems, alternative solutions such as aerial LiDAR or photogrammetric approaches could also be explored as potential alternatives to MLS data in high-definition mapping for autonomous vehicles.
1.2. Probabilistic-based multi-objective optimization
Probabilistic-based multi-objective optimization has become a widely-used approach for optimizing complex systems, including those associated with Light Detection and Ranging (LiDAR) technology [8], [25, 26]. LiDAR is a popular remote sensing method used for vegetation mapping, terrain analysis, and even in self-driving cars. However, optimizing LiDAR systems is complicated by the existence of multiple, often competing, objectives that need to be considered, such as accuracy, resolution, and cost. Hence, probabilistic-based multi-objective optimization offers a promising solution to identify the trade-offs between these objectives and find optimal solutions that satisfy multiple criteria at once [27-29]. This approach allows for consideration of uncertainties in the LiDAR observations and model parameters, ultimately leading to improved LiDAR system design and performance. 
On the other hand, the process systems engineering literature has recently noticed a growing interest in optimization under unpredictability and uncertainty. The presence of uncertainties in an optimization model necessitates the management of the potential consequences in order to control the variability of the objective function in the uncertain specifications and parameter space [30]. To fill these gaps, this study concentrates on using dense MLS point maps and semantically labelled image datasets generated by MLS point clouds [10], [29], [31, 32]. To generate accurate probabilistic semantic maps for urban driving scenarios (i.e., reliable road labels, lane markings, pedestrian crossings, and sidewalks), we focus on training the model solely on a publicly available dataset. We incorporated uncertainty simulation through a confusion matrix formulation of the semantic segmentation network to create a Bird's-Eye-View (BEV) semantic map of the environment. Furthermore, we achieved improved lane mark segmentation precision on the roadways by employing a probabilistic Pareto-type optimization approach and fusing LiDAR intensity. In addition, we developed a multi-objective stochastic model with a collection of risk measures. Finally, this study evaluated the generated solutions with Pareto optimization, which identifies solutions with higher performance in parameter spaces with unknown values.
In this context, probabilistic-based multi-objective optimization has been introduced as a technique to enhance the quality of the maps generated by mobile LiDAR. By considering multiple objectives such as accuracy, density, and efficiency, probabilistic-based multi-objective optimization can provide a comprehensive and adaptable solution for creating high-definition maps. We present a new approach for creating high-definition maps using mobile LiDAR point clouds and probabilistic-based multi-objective optimization and propose a complete framework that combines the strengths of mobile LiDAR with probabilistic-based multi-objective optimization to generate high-precision, high-density maps with low computational cost. To assess the efficacy of our approach, we conduct evaluations on multiple datasets and provide a comparative analysis against existing methods.
This paper introduces a substantial contribution to high-definition mapping by proposing a holistic approach that leverages mobile LiDAR and probabilistic-based multi-objective optimization. By combining these strengths, the proposed approach has the potential to greatly enhance the effectiveness and efficiency of creating high-definition maps for autonomous vehicles. In addition, we explore the principles of probabilistic-based multi-objective optimization in the context of LiDAR technology [33], including its potential benefits and challenges.
In terms of differences from the existing literature, this study focuses on leveraging mobile LiDAR systems to capture high-density point clouds in complex urban environments, and it introduces a comprehensive framework that combines the strengths of mobile LiDAR and probabilistic-based multi-objective optimization for enhanced map creation. This study presents several advantages, including the integration of mobile LiDAR technology and probabilistic-based multi-objective optimization, leading to highly accurate and dense high-definition maps for autonomous vehicles. By considering critical factors such as autonomous ability, swift data acquisition, minimal error, and dense point clouds, the study enhances the effectiveness and efficiency of map generation. Additionally, the proposed approach incorporates a novel multi-objective stochastic model, integrating various risk measures to enhance the reliability of the generated maps.
 In summary, this study presents several significant contributions, including the utilization of Mathematics of Lidar Sensing (MLS) and probabilistic-based multi-objective optimization to generate highly accurate, high-definition (HD) maps, effectively balancing critical factors such as autonomous ability, swift data acquisition, minimal error, and dense point clouds. By implementing a Pareto optimal approach, we can effectively balance critical factors such as autonomous ability, swift data acquisition, minimal error, and dense point clouds. Moreover, we developed a novel multi-objective stochastic model, integrating a range of risk measures to enhance reliability. Our methodology has resulted in a marked increase in map accuracy, with improvements of 11% on roads, 8% on crosswalks, and 30% in lane markings. These outcomes demonstrate the effectiveness of our approach and provide meaningful insights to advance the application of probabilistic-based multi-objective optimization in LiDAR-based HD map generation. The rest of the paper is structured to explain the method and proposed model in Section 2, followed by experimental results and implementation in Section 3. Then, results and discussion are given in Section 4, and Section 5 concludes the paper.