Roadmap Construction Research Articles

Targeting Lane-Level Map Matching for Smart Vehicles: Construction of High-Definition Road Maps Based on GIS

The development of smart vehicles has increased the demand for high-definition road maps. However, traditional road maps for vehicle navigation systems are not sufficient to meet the requirements of intelligent vehicle systems (e.g., autonomous driving). The present work comes up with a method of generating high-definition map models based on the geographic information system (GIS). A systematic map construction framework including the road layer, intersection connection layer, and lane layer is proposed based on the GIS database. Specifically, the constrained Delaunay triangular network method is applied to extract road layer network models, which are then used as linear reference networks to construct lane-level road maps. To further examine the feasibility of the proposed framework, a field experiment is then conducted to build a high-definition road map. Furthermore, a lane-level map matching test is conducted in the constructed road map using the trajectory data collected from a probe vehicle. The results show that the proposed method provides an efficient way of extracting lane-level information from urban road networks and can be applied for lane-level map matching with good performance.

Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

In recent years, the construction of lane-level road maps has received extensive attention from industry and academia. It has been widely studied because this kind of map provides the foundation for much research, such as high-precision navigation, driving behavior analysis, and traffic analysis. Trajectory-based crowd-mapping is an emerging approach to lane-level map construction. However, the major problem is that existing methods neglect modeling the trajectory distribution in the longitudinal direction of the road, which significantly impacts precision. Thus, this article proposes a two-stage method based on vehicle lane-changing behavior to model the road’s lateral and longitudinal trajectory distributions simultaneously. In the first stage, lane-changing behaviors are extracted from vehicle trajectories. In the second stage, the lane extraction model is established using the weighted constrained Gaussian mixture model and hidden Markov model to estimate lane parameters (e.g., lane counts and lane centerline) on each road cross section. Then accurate and continuous lane centerlines can be constructed accordingly. The proposed method is verified using vehicle trajectory data collected from the crowdsourced platform named Mapillary. The results show that the proposed method can construct lane-level road information satisfactorily.

Hierarchical Segmentation Method for Generating Road Intersections from Crowdsourced Trajectory Data

Maintaining the data freshness and completeness of road intersection information is the key task of urban road map production and updating. Compared to professional surveying methods, crowdsourced trajectory data provide a low-cost, wide-coverage and real-time data resource for road map construction. However, there may exist the problems of spatio-temporal heterogeneity and uneven density distribution in crowdsourced trajectory data. Hence, in light of road hierarchies, the paper proposes a hierarchical segmentation method to generate road intersections from crowdsourced trajectories. The proposed method firstly implements an adaptive density homogenization processing on raw trajectory data in order to decrease the uneven density discrepancy. Then, a hierarchical segmentation strategy is developed to extract multi-level road intersection elements from coarse scale to fine scale. Finally, the structural models of road intersections are delineated by an iterative piecewise fitting method. Experimental results show that the proposed method can accurately and completely extract road intersections of different shapes and scales, with an accuracy of about 87–90%. Particularly, the precision and recall of road intersection detection are obviously increased by about 7% and 20% by adaptive density homogenization, indicating the advantages of dealing with uneven trajectory data.

Hierarchical Planning With Annotated Skeleton Guidance

We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant degrees of freedom and is able to guide the planner to find the desired solutions fast. However, sometimes the skeleton does not closely represent the free c-space, which often misleads current skeleton-guided planners. The hierarchical skeleton-guided planning strategy gradually relaxes its reliance on the workspace skeleton as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {C}_{space}$</tex-math></inline-formula> is sampled, thereby incrementally returning a sub-optimal path, a feature that is not guaranteed in the standard skeleton-guided algorithm. Experimental comparisons to the standard skeleton guided planners and other lazy planning strategies show significant improvement in roadmap construction run time while maintaining path quality for multi-query problems in cluttered environments.

A Long Short-Term Memory-Based Approach for Detecting Turns and Generating Road Intersections from Vehicle Trajectories.

Owing to the widespread use of GPS-enabled devices, sensing road information from vehicle trajectories is becoming an attractive method for road map construction and update. Although the detection of intersections is critical for generating road networks, it is still a challenging task. Traditional approaches detect intersections by identifying turning points based on the heading changes. As the intersections vary greatly in pattern and size, the appropriate threshold for heading change varies from area to area, which leads to the difficulty of accurate detection. To overcome this shortcoming, we propose a deep learning-based approach to detect turns and generate intersections. First, we convert each trajectory into a feature sequence that stores multiple motion attributes of the vehicle along the trajectory. Next, a supervised method uses these feature sequences and labeled trajectories to train a long short-term memory (LSTM) model that detects turning trajectory segments (TTSs), each of which indicates a turn occurring at an intersection. Finally, the detected TTSs are clustered to obtain the intersection coverages and internal structures. The proposed approach was tested using vehicle trajectories collected in Wuhan, China. The intersection detection precision and recall were 94.0% and 91.9% in a central urban region and 94.1% and 86.7% in a semi-urban region, respectively, which were significantly higher than those of the previously established local G* statistic-based approaches. In addition to the applications for road map development, the newly developed approach may have broad implications for the analysis of spatiotemporal trajectory data.

Real-Time Path Planning for Robot Using OP-PRM in Complex Dynamic Environment.

During task execution, the autonomous robots would likely pass through many narrow corridors along with mobile obstacles in dynamically complex environments. In this case, the off-line path planning algorithm is rather difficult to be directly implemented to acquire the available path in real-time. Hence, this article proposes a probabilistic roadmap algorithm based on the obstacle potential field sampling strategy to tackle the online path planning, called Obstacle Potential field-Probabilistic Roadmap Method (OP-PRM). The obstacle potential field is introduced to determine the obstacle area so as to construct the potential linked roadmap. Then the specific range around the obstacle boundary is justified as the target sampling area. Based on this obstacle localization, the effectiveness of the sampling points falling into the narrow corridors can be increased greatly for feasible roadmap construction. Furthermore, an incremental heuristic D* Lite algorithm is applied to search the shortest paths between the starting point and the target point on the roadmap. Simulation experiments demonstrate that the OP-PRM path planning algorithm can enable robots to search the optimal path fast from the starting point to the destination and effectively cross narrow corridors in complex dynamic environments.

Mining crowdsourced trajectory and geo‐tagged data for spatial‐semantic road map construction

AbstractThe road map is a fundamental part of a spatial data infrastructure (SDI), and is widely applied in navigation, smart transportation, and mobile location services. Recently, with the ubiquity of positioning devices, crowdsourced trajectories have become a significant data resource for road map construction and updating. However, existing trajectory‐based methods mainly place emphasis on extracting road geometry features and may ignore continuous updating of road semantic information. Hence, we propose a divide‐and‐conquer method to construct a spatial‐semantic road map by incorporating multiple data sources (e.g., crowdsourced trajectories and geo‐tagged data). The proposed method divides road map construction into two sub‐tasks, road structure reconstruction and road attributes inference. The road structure reconstruction process starts to partition raw trajectory data into different cliques of roadways and road intersections, and then extracts various targeted road structures by analyzing the turning modes in different trajectory cliques. The road attributes inference process aims to infer three pieces of crucial semantic information about road speeds, turning rules, and road names from crowdsourced trajectories and geo‐tagged data. The case studies in Wuhan were examined to illustrate that the proposed method can construct a routable road map with enhanced geometric structures and rich semantic information, providing a beneficial data solution for car navigation and SDI update.

Generative Methods for Guard and Camera Placement in Stealth Games

Enemy observers, such as cameras and guards, are common elements that provide challenge to many stealth and combat games. Defining the exact placement and movement of such entities, however, is a non-trivial process, requiring a designer balance level-difficulty, coverage, and representation of realistic behaviours. In this work we explore systems for procedurally generating both camera and guard placement in a stealth game context. For the former we use an approach based on weakening theoretical results for optimal camera placement, while for the latter we perform automatic roadmap construction, generating more specific patrol behaviours through a grammar-based technique. We evaluate both approaches with a non-trivial implementation in Unity3D, and apply quantitative metrics to demonstrate how different parametrizations can be used to control level difficulty without sacrificing believability.

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

We propose an integrated path planning method for multiple automated guided vehicles performing logistics delivery within a real-world warehouse environment considering obstacles. By applying it on each vehicle, this proposed method enables the vehicle the vehicles have the capabilities for autonomous path planning. The path planning consists of three parts, K-means algorithm based task points clustering, genetic algorithm based task points ordering, and the probabilistic road map based best path search. Vehicle conflict resolution is depending on implementing the probabilistic road map construction considering the realistic map with obstacles. The simulations result validate that the clustering and ordering are necessary for the path planning, both the path planning time and the Automated guided vehicles (AGVs) running time can be dramatically reduced.

Topology-Guided Roadmap Construction With Dynamic Region Sampling

Many types of planning problems require discovery of multiple pathways through the environment, such as multi-robot coordination or protein ligand binding. The Probabilistic Roadmap (PRM) algorithm is a powerful tool for this case, but often cannot efficiently connect the roadmap in the presence of narrow passages. In this letter, we present a guidance mechanism that encourages the rapid construction of well-connected roadmaps with PRM methods. We leverage a topological skeleton of the workspace to track the algorithm's progress in both covering and connecting distinct neighborhoods, and employ this information to focus computation on the uncovered and unconnected regions. We demonstrate how this guidance improves PRM's efficiency in building a roadmap that can answer multiple queries in both robotics and protein ligand binding applications.

항공사진을 이용한 정밀도로지도 구축에 관한 연구

자율주행기술은 4차 산업혁명을 대표하는 기술로 주목되고 있음에 따라 자율주행차 지원을 위한 정밀도로지도 구축에 많은 투자가 이루어지고 있다. 현재 정밀도로지도는 지상 mobile mapping systems(MMS) 측량을 통해 구축되고 있지만, 장비 및 비용·효율의 한계가 존재한다. 이 연구는 정밀도로지도 구축에 있어 항공사진을 이용하여 구축된 정밀도로지도 분석을 통해 정밀도로지도 구축 가능성을 검토하였다. 검토 결과 지상표본거리 12cm 항공사진을 이용할 경우 지상표본거리 25cm 항공사진보다 구축 가능한 레이어 유형의 범위가 넓었으며, 지상표본거리 12cm 항공사진을 통해 구축된 정밀도로지도의 정밀도를 분석한 결과 평면오차는 0.15m, 수직오차는 0.20m 이내로 확인되었다. 이는 국토지리정보원의 정밀도로지도 품질검사 기준인 벡터데이터 절대정확도 0.25m를 모두 만족하는 수치로 나타났다. 레이어 구축범위 및 정밀도 분석결과를 고려해볼 때 정밀도로지도 구축 시 지상 MMS 측량 외 항공사진측량을 필요에 따라 활용할 경우 MMS장비 한계의 보완과 효율적인 정밀도로지도 제작·갱신이 가능할 것으로 기대된다.

Construction of generic roadmaps for the strategic coordination of global research into infectious diseases of animals and zoonoses.

The Strategic Alliance for Research into Infectious Diseases of Animals and Zoonoses (STAR-IDAZ) International Research Consortium (IRC) coordinates global animal health research to accelerate delivery of disease control tools and strategies. With this vision, STAR-IDAZ IRC has constructed four generic research roadmaps for the development of candidate vaccines, diagnostic tests, therapeutics and control strategies for animal diseases. The roadmaps for vaccines, diagnostic tests and therapeutics lead towards a desired target product profile (TPP). These interactive roadmaps describe the building blocks and for each the key research questions, dependencies, challenges and possible solution routes to identify the basic research needed for translation to the TPP. The control strategies roadmap encompasses the vaccine, diagnostic tests, and therapeutic roadmaps within a wider framework focusing on the inter-dependence of multiple tools and knowledge to control diseases for the benefit of animal and human health. The roadmaps are now being completed for specific diseases and complemented by state-of-the-art information on relevant projects and publications to ensure that the necessary research gaps are addressed for selected priority diseases.

Biopharmaceutical Understanding of Excipient Variability on Drug Apparent Solubility Based on Drug Physicochemical Properties. Case Study: Superdisintegrants

The presence of different excipient types/brands in solid oral dosage forms may affect product performance and drug bioavailability. Understanding the biopharmaceutical implications of superdisintegrant variability (changes in material properties), variation (changes in excipient amount) and interchangeability (use of different excipient types with the same intended functionality) in oral drug performance would be beneficial for the development of robust final dosage forms. The current study investigated the impact of superdisintegrants (sodium starch glycolate, croscarmellose sodium, crospovidone) on the apparent solubility of drugs with different physicochemical properties (drug ionisation, drug lipophilicity, drug aqueous solubility). Compendial and biorelevant media were used to assess the impact of gastrointestinal conditions on the effects of excipient on drug apparent solubility. For the majority of compounds, changes in drug apparent solubility were not observed in superdisintegrant presence, apart from the cases of highly ionised compounds (significant decrease in drug solubility) and/or compounds that aggregate/precipitate in solution (significant increase in drug solubility). Excipient variability did not greatly affect the impact of excipients on drug apparent solubility. The use of multivariate data analysis identified the biopharmaceutical factors affecting excipient performance. The construction of roadmaps revealed that superdisintegrants may be of low risk for the impact of excipients on oral drug performance based on drug solubility alone; superdisintegrants activity could still be a risk for oral bioavailability due to their effects on tablet disintegration.

HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots

Path planning and navigation is a very important problem in robotics, especially for mobile robots operating in complex environments. Sampling based planners such as the probabilistic roadmaps (PRM) have been widely used for different robot applications. However, due to the random sampling of nodes in PRM, it suffers from narrow passage problem that generates unconnected graph. The problem is addressed by increasing the number of nodes but at higher computation cost affecting real-time performance. To address this issue, in this paper, we propose an improved sampling-based path planning method for mobile robot navigation. The proposed method uses a layered hybrid Probabilistic Roadmap (PRM) and the Artificial Potential Field (APF) method for global planning. We used a decomposition method for node distribution that uses map segmentation to produce regions of high and low potential, and propose a method of reducing the dispersion of sample set during the roadmap construction. Our method produces better goal planning queries with a smaller graph and is computationally efficient than the traditional PRM. The proposed planner called the Hybrid Potential based Probabilistic Roadmap (HPPRM) is an improved sampling method with respect to success rate and calculation cost. Furthermore, we present a method for reactive local motion planning in the presence of static and dynamic obstacles in the environment. The advantage of the proposed method is that it can avoid local minima and successfully generate plans in complex maps such as narrow passages and bug trap scenarios that are otherwise difficult for the traditional sample-based methods. We show the validity of our method with experiments in simulation and real environments for both local and global planning. The results indicate that the proposed HPPRM is effective for autonomous mobile robot navigation in complex environments. The success rate of the proposed method is higher than 95% both for local and global planning.

Autonomous Robotic Exploration by Incremental Road Map Construction

In this paper, we propose a novel path planning framework for autonomous exploration in unknown environments using a mobile robot. A graph structure is incrementally constructed along with the exploration process. The structure is the road map that represents the topology of the explored environment. To construct the road map, we design a sampling strategy to get random points in the explored environment uniformly. A global path from the current location of the robot to the target area can be found on this road map efficiently. We utilize a lazy collision checking method that only checks the feasibility of the generated global path to improve the planning efficiency. The feasible global path is further optimized with our proposed trajectory optimization method considering the motion constraints of the robot. This mechanism can facilitate the path cost evaluation for the next best view selection. In order to select the next best target region, we propose a utility function that takes into account both the path cost and the information gain of a candidate target region. Moreover, we present a target reselection mechanism to evaluate the target region and reduce the extra path cost. The efficiency and effectiveness of our approach are demonstrated using a mobile robot in both simulation and real experimental studies. Note to Practitioners —This paper is motivated by the efficient exploration problem, which plays a key role in various areas such as the information gathering and environment monitoring. In these applications, the mission length and executing time are often restricted by the battery capacity of the robot. In this paper, an efficient path planning framework is introduced to reduce the path length and exploration time. The robot keeps a road map of the environment to facilitate the path planning. The proposed target selection mechanism helps the robot determine the next best target to explore. Furthermore, the proposed trajectory optimization algorithm helps in reducing the path cost. Overall, this framework enables efficiently and autonomously exploration with a novel path planning framework.

Constructing and exploring the model to form the road map of enterprise development

The study addresses the task on determining the optimal road map for the enterprise development – the choice of the sequence of projects and their parameters, which would ensure the achievement of the goal taking into consideration the basic requirements and constraints. The optimization of the structure and parameters of the development road map is carried out based on the that makes it possible to form alternative variants of the road map of development. This network is based on the principle of the formation of transport networks, and the space – indicators of the enterprise is accepted as the analogue of the topological space.The elements of the strategic network – nodes – are the enterprise states and the projects that correspond to network transitions from one state to another. The network parameters include: duration of transition from state to state, determined by the intensity of project activities; duration of the project funding process; beginning of the project implementation (transition to a new stage). These parameters determine the following characteristics of the projects that form the characteristics of the road map: project costs; root mean square deviation of project costs; financial result after the project implementation; root mean square deviation of the financial result after the project implementation.The model for establishing the optimal structure and parameters of the road map of the enterprise development was developed. This model makes it possible to determine the optimal road map of the enterprise development, taking into consideration the possibility of varying the time parameters: time of beginning of each stage, duration of transition from stage to stage, duration of funding phases. The model takes into consideration the probabilistic nature of investment costs and inflows of the funds of an enterprise after the implementation of development activities.Experimental studies on the formation of a road map based on the developed model were carried out, which proved its operation capacity and reliability. The model can find a wide practical application in solving the issues on the construction of road maps of a step-by-step increase in production, or a phased replacement of equipment (technical development)

Collision-Free Path Planning and Delivery Sequence Optimization in Noncoplanar Radiation Therapy.

Radiation therapy is among the top three cancer treatments in current medical services. The novel noncoplanar radiation therapy which claimed the best characteristics in almost all dosimetric properties encountered the challenges of the potential collision and the long time delivering. In this paper, we proposed a brand new scheme which uses a combined method of the collision avoidance path planning based on an improved probability roadmap method (PRM) and the delivery sequence optimization based on a modified genetic algorithm (GA) to solve the problems in noncoplanar radiation therapy. A uniform sampling strategy, an improved connection strategy, and an efficient local planner are introduced to optimize the roadmap result and accelerate the roadmap construction. The GA is improved by the elitist selection, the local search strategy, and the similar substitution strategy to achieve a better performance both in convergence rate and optimal solution. Experiments are carried out on the simulation platform with typical therapy system models. The results show that our proposed methods work well with the radiation therapy system in a compact working area. Collision is avoided and time consumption is reduced. We believe that our proposed algorithms could solve the problems in current radiation therapy and promote their clinic applications.

Image-guided lacrimal drainage surgery in congenital arhinia-microphthalmia syndrome

ABSTRACTThe aim of this study was to illustrate the surgical techniques and utility of stereotactic or image-guided navigation in the management of lacrimal drainage obstruction in congenital arhinia-microphtalmia syndrome and review the relevant literature. Image-guided combined external and endoscopic dacryocystorhinostomy was performed in a female, aged 16 years with congenital partial arhinia and ipsilateral microphthalmus. The lacrimal sac was bypassed to the contra lateral nasal cavity through a septal window. The surgical procedure was performed using the intra-operative optical image-guided Nav 1 PicoTM ENT navigation system with real-time intra-operative instrument geometry. Different phases of the surgical technique, adjunctive endoscopic procedures, intra-operative anatomical guidance, and utility at crucial phases of surgery were noted. A review of the literature was performed pertinent to arhinia and navigation guided lacrimal surgeries. Lacrimal bypass into the contra lateral nasal cavity even through a malformed septum is possible in partial arhinia syndromes. Detailed preoperative evaluation including 3D imaging studies, navigation guided planning of risk structures with intra-operative distance control and construction of meticulous surgical roadmaps were found to be essential factors in successful outcomes. At six months follow up after surgery, there was a complete and contiguous healed mucosal anastomosis with lacrimal system patent on irrigation and resolution of epiphora. Combined external and endoscopic approach is useful in partial arhinia syndromes. Image guidance is a very useful adjunctive tool that facilitates safe and precise surgery in the management of such complex lacrimal surgeries.

A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

With the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis in Wuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy.

Solar PV diffusion in Argentina: policy implications for a high penetration scenario until 2030

This paper aims at proposing a methodological approach to identify the minimal contents for a future solar photovoltaic (PV) roadmap in Argentina, focusing on the actions required to reach a high penetration scenario until 2030. Argentina has been facing energy supply shortages caused by a combination of increasing in energy demand and government inability to meet the additional demand on time with the available resources (mainly fossil fuels). Although versatile and scalable, Solar PV technology has been receiving low incentives from Argentinean government. Two literature’s perspectives - the functional perspective of technological innovation systems and prospective studies (mainly normative scenarios and roadmaps as a planning tool) – were applied to set the proposed high penetration scenario up to 2030. Stakeholders and expert’s visions, global path of technology and a local prospective study made by local experts in 2012 were used to validate the proposed scenario. As main results, a way to build a future solar photovoltaic scenario in urban and suburban areas is described; minimal contents that can orient a local roadmap construction are defined, and the necessary improvements of solar PV framework in Argentina (both regulatory and promotion aspects) are pointed out.