Spatiotemporal Planning Research Articles

Spatio-Temporal Corridor-Based Motion Planning of Lane Change Maneuver for Autonomous Driving in Multi-Vehicle Traffic

Spatio-Temporal Corridor-Based Motion Planning of Lane Change Maneuver for Autonomous Driving in Multi-Vehicle Traffic

IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

IR-STP: Enhancing Autonomous Driving With Interaction Reasoning in Spatio-Temporal Planning

Spread management priorities to limit emerald ash borer (Agrilus planipennis) impacts on United States street trees

AbstractThe invasive emerald ash borer (Agrilus planipennis) causes damage to street trees which is estimated to reach US$ 900 million over the next 30 years. Although millions of dollars are spent annually to control this species, spatiotemporal management plans are often based on rules of thumb that ignore future pest dispersal. Here, we reveal an optimal management strategy to protect urban trees in North America from A. planipennis. To achieve this, we embedded a pest dispersal model within a mixed integer programming framework. We discovered that optimized strategies consistently outperformed those based on rules of thumb, potentially resulting in the protection of an additional nearly 1 million street trees and savings of $ 629 million. Critically, the best management strategies always relied on quarantines and biological control (constituting 98–99% and 1–2% of the project budget, respectively), in contrast with current practices, where federal spending has been diverted to biological control. Our findings serve to inform future pest control efforts and can help protect many more trees from this invasive species.

XR4DRAMA a knowledge-based system for disaster management and media planning

Abstract In the previous two decades, Knowledge Graphs (KGs) have evolved, inspiring developers to build ever-more context-related KGs. Because of this development, Artificial Intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, we introduce the XR4DRAMA framework. The KG of the XR4DRAMA framework can represent data for media preparation and disaster management. More specifically, the KG of the XR4DRAMA framework can represent information about: (a) Observations and Events (e.g., data collection of biometric sensors, information in photos and text messages), (b) Spatio-temporal (e.g., highlighted locations and timestamps), (c) Mitigation and response plans in crisis (e.g., first responder teams). In addition, we provide a mechanism that allows Points of Interest (POI) to be created or updated based on videos, photos, and text messages sent by users. For improved disaster management and media coverage of a location, POI serve as markers to journalists and first responders. A task creation mechanism is also provided for the disaster management scenario with the XR4DRAMA framework, which indicates to first responders and citizens what tasks need to be performed in case of an emergency. Finally, the XR4DRAMA framework has a danger zone creation mechanism. Danger zones are regions in a map that are considered as dangerous for citizens and first responders during a disaster management scenario and are annotated by a severity score. The last two mechanisms are based on a Decision Support System (DSS).

STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds

This article proposes a means of autonomous mobile robot navigation in dense crowds based on predicting pedestrians’ future trajectories. The method includes a pedestrian trajectory prediction for a running mobile robot and spatiotemporal path planning for when the path crosses with pedestrians. The predicted trajectories are converted into a time series of cost maps, and the robot achieves smooth navigation without dodging to the right or left in crowds; the path planner does not require a long-term prediction. The results of an evaluation implementing this method in a real robot in a science museum show that the trajectory prediction works. Moreover, the proposed planning’s arrival times is 26.4% faster than conventional 2D path planning’s arrival time in a simulation of navigation in a crowd of 50 people.

Multicriteria Analysis in Apiculture: A Sustainable Tool for Rural Development in Communities and Conservation Areas of Northwest Peru

Apiculture plays a vital role in maintaining a genetically diverse ecosystem and is an economic activity that contributes to the development of rural communities, thereby enhancing the livelihoods of beekeepers. However, despite the presence of over forty thousand beekeepers in Peru, there is currently no cartographic information available on optimal areas for the development of apiculture. Our study focused on assessing the suitability of land for apiculture development in rural and indigenous communities within the Amazonas Department in northwest Peru. We integrated biophysical and socioeconomic criteria using the Multiple Criteria Evaluation (MCE) technique, in conjunction with state-of-the-art geoinformation and earth observation techniques, to model and validate land suitability for supporting apiculture. It was identified that suitability is influenced by biophysical criteria (65%) and socioeconomic criteria (35%), resulting in highly suitable areas covering 315.6 km2 within the territory of peasant communities, 128.4 km2 within native communities, and an additional 41.4 km2 within conserved areas. Furthermore, to validate our results, we combined the use of high-resolution satellite imagery and visits to artisanal producers. This research provides valuable insights for spatiotemporal land use planning, emphasizing apicultural activity as a driver of rural development and biodiversity conservation. Consequently, this study contributes as a management tool to promote apicultural activities as support for rural development and in local-level decision making.

Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning

Combat situation suppression of multiple UAVs based on spatiotemporal cooperative path planning

From lockdown to precise prevention: Adjusting epidemic-related spatial regulations from the perspectives of the 15-minute city and spatiotemporal planning

The COVID-19 pandemic challenged emergency management in cities worldwide. Many municipalities adopted restrictive, one-size-fits-all spatial regulations such as lockdowns without fully considering the inhabitants’ daily activities and local economies. The existing epidemic regulations’ unintended detrimental effects on socioeconomic sustainability necessitate a transition from the “lockdown” approach to more precise disease prevention. A spatially and temporally precise approach that balances epidemic prevention with the demands of daily activities and local economies is needed. Thus, the aim of this study was to propose a framework and key procedures for determining precise prevention regulations from the perspectives of the 15-minute city concept and spatiotemporal planning. Alternative regulations of lockdowns were determined by delineating 15-minute neighborhoods, identifying and reconfiguring facility supplies and activity demands in both normal and epidemic conditions, and performing cost-benefit analyses. Highly adaptable, spatially- and temporally-precise regulations can match the needs of different types of facilities. We demonstrated the process for determining precise prevention regulations in the case of the Jiulong 15-minute neighborhood in Beijing. Precise prevention regulations—which meet essential activity demands and are adaptable for different facility types, times, and neighborhoods—have implications for long-term urban planning and emergency management.

View-Invariant Spatiotemporal Attentive Motion Planning and Control Network for Autonomous Vehicles

Autonomous driving vehicles (ADVs) are sleeping giant intelligent machines that perceive their environment and make driving decisions. Most existing ADSs are built as hand-engineered perception-planning-control pipelines. However, designing generalized handcrafted rules for autonomous driving in an urban environment is complex. An alternative approach is imitation learning (IL) from human driving demonstrations. However, most previous studies on IL for autonomous driving face several critical challenges: (1) poor generalization ability toward the unseen environment due to distribution shift problems such as changes in driving views and weather conditions; (2) lack of interpretability; and (3) mostly trained to learn the single driving task. To address these challenges, we propose a view-invariant spatiotemporal attentive planning and control network for autonomous vehicles. The proposed method first extracts spatiotemporal representations from images of a front and top driving view sequence through attentive Siamese 3DResNet. Then, the maximum mean discrepancy loss (MMD) is employed to minimize spatiotemporal discrepancies between these driving views and produce an invariant spatiotemporal representation, which reduces domain shift due to view change. Finally, the multitasking learning (MTL) method is employed to jointly train trajectory planning and high-level control tasks based on learned representations and previous motions. Results of extensive experimental evaluations on a large autonomous driving dataset with various weather/lighting conditions verified that the proposed method is effective for feasible motion planning and control in autonomous vehicles.

Reinforcement learning-based simulation and automation for tower crane 3D lift planning

Tower crane lift planning is important to timely provide resources to workplaces. However, previous planning approaches are still impractical because the lifting time of a plan is barely considered and the lifting path is frequently non-executable by operators. This paper describes a reinforcement learning-based method that incorporates the actuator system of a tower crane into spatio-temporal lift planning in three-dimensional virtual environments wherein various strategies of algorithm types and learning rules are tested. The results show stable and practical lift planning with a failure ratio of 3%, coordination ratio of 28%, and positive evaluation of lifting procedures by expert operators. In addition, the estimated lifting time shows a correlation of 0.6857 with the actual time from field observation. Thus, the proposed approach is promising for planning feasible lifting paths and estimating reasonable lifting times, which help generate and review lifting plans given the site conditions.

A Mean-Field Game Control for Large-Scale Swarm Formation Flight in Dense Environments.

As an important part of cyberphysical systems (CPSs), multiple aerial drone systems are widely used in various scenarios, and research scenarios are becoming increasingly complex. However, planning strategies for the formation flying of aerial swarms in dense environments typically lack the capability of large-scale breakthrough because the amount of communication and computation required for swarm control grows exponentially with scale. To address this deficiency, we present a mean-field game (MFG) control-based method that ensures collision-free trajectory generation for the formation flight of a large-scale swarm. In this paper, two types of differentiable mean-field terms are proposed to quantify the overall similarity distance between large-scale 3-D formations and the potential energy value of dense 3-D obstacles, respectively. We then formulate these two terms into a mean-field game control framework, which minimizes energy cost, formation similarity error, and collision penalty under the dynamical constraints, so as to achieve spatiotemporal planning for the desired trajectory. The classical task of a distributed large-scale aerial swarm system is simulated by numerical examples, and the feasibility and effectiveness of our method are verified and analyzed. The comparison with baseline methods shows the advanced nature of our method.

Spatiotemporal optimisation of prostate intensity modulated proton therapy (IMPT) treatments

Objective. In intensity modulated particle therapy (IMPT), the adoption of spatially and temporally heterogeneous dose distributions allows to decouple the fractionation scheme from the patient anatomy, so that an hypofractionated schedule can be selectively created inside the tumour, while simultaneously exploiting the fractionation effect in the healthy tissues. In this paper, the authors show the reproducibility of the method on a set of prostate patients, quantifying the dependencies of the achievable benefit with respect to conventional and hypofractionated schemes and the sensitivity of the method to setup errors and range uncertainty. Approach. On a cohort of 9 patients, non-uniform IMPT plans were optimised and compared to conventional and hypofractionated schedules. For each patient, the comparison of the three strategies has been based on the output of the cost function used to optimise the treatments. The analysis has been repeated considering different α/β ratios for the tumour, namely 1.5, 3 and 4.5 Gy. For a single patient, setup errors and beam range uncertainty have been analysed: the plans, for each optimisation strategy, have been iteratively forward planned 500 times with randomly varying the patient position in each fraction, and 200 times for systematic range shift. Main results. An average 10% benefit has been shown for the lowest α/β ratio considered for the tumour, where the non-uniform schedule generally converges to hypofractionation; the benefit decreases to 5%–7% for higher α/β ratios, for which the non-uniform schedule always showed better outcomes with respect to the other fractionation schedules. An increased sensitivity to uncertainty, especially for setup errors, has been shown, which can be associated to the spatial non-uniformity of the dose distributions peculiar of the spatiotemporal plans. Significance. This work represents the first investigation of spatiotemporal fractionation for prostate cancer and the beginning of further investigations before clinical implementation can be considered.

Spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search

A spatio-temporal coverage planning algorithm for multi-UAV and multi-sensor cooperative search is proposed in this paper, named STC-MARL, which is based on multi-agent reinforcement learning and allows several UAVs equipped with two types of sensors to learn to complete full view of a field of interest (FOI). The proposed STC-MARL algorithm can make participating UAVs to learn from the environment to complete the full coverage of a specific FOI while minimizing field of views (FOVs) interacted with each other. The experimental results show in detail with simulation that the UAVs in the mission can effectively accomplish the task of spatio-temporal coverage planning.

Spatio-temporal planning simulation of temporary elevators in construction using a game engine

Designing a reasonable and efficient transportation system of temporary elevators from the perspective of spatio-temporal planning is beneficial for the successful completion of construction projects. Specifically, the determination of the service period and floor of temporary elevators and the consideration of the limited availability of time and space should be taken seriously. The spatio-temporal planning of temporary elevators puts forward higher requirements for the display of planning solutions due to the complexity considering both time and space. However, current display ways are generally abstract, low-dimensional, and static, and even when the 4D model is adopted, the display effect of spatio-temporal planning solutions is still limited. This research presents a spatio-temporal planning simulation of temporary elevators in construction using a game engine to address those challenges. The workflow of the spatio-temporal planning simulation divided into configuration and operation planning consists of three steps: 1) import data into the game engine platform, 2) generate the spatio-temporal planning simulation scheme, and 3) conduct the spatio-temporal planning simulation. The results of the experimental test show that the presented spatio-temporal planning simulation way is intuitive and convenient, various planning elements are clearly shown, and corresponding planning information is efficiently conveyed, which can effectively enhance the display effect of spatio-temporal planning solutions.

Drought Disaster Risk Adaptation through Ecosystem Services-Based Solutions: Way Forward for South Africa

Ecosystem services refer to the direct and indirect benefits to humanity from an ecosystem. The ability to spatially incorporate multiple biophysical environments is crucial to ecosystem services, thus promoting cooperation between science and policy in seeking solutions to global challenges, including drought disasters. Therefore, understanding ecosystem services, for instance, from forest/vegetation in view of contributing to drought disaster risk adaptation is critical to human-nature interactions and proper sustainable conservation thereof. No known study has been done on ecosystem services and their contributions to drought management or other climate adaptation in South Africa. This study aimed at quantifying drought disaster risk adaptation based on ecosystem services in South Africa. It was identified that ecosystem services to society have been directly affected by anthropogenic and natural phenomena, thereby influencing drought severity and its impacts. These impacts and their associated risks are evident globally, including in South Africa. We found out that ecosystems in South Africa have been affected and extremely vulnerable to recurrent natural disasters, such as droughts. To achieve long-term solutions to such drought-related risks and challenges, feedback mechanisms between human-natural and related factors and ecosystem services-based drought adaptation need to be understood and planned. Timely spatiotemporal assessment, planning and management strategies need to be considered to find solutions or ways forward to South Africa in combating drought disasters.

AN ANALYSIS ON THE EFFECTS OF CORONAVIRUS (COVID-19) ON THE FINANCIAL MANAGEMENT OF MULTINATIONAL CORPORATIONS

This paper develops a sensible framework to get a handle on the spatiotemporal plans of the COVID-19 event, its real nature, and its implications to the Financial Management of MNCs (Multinational Corporations). In the most straightforward structure, the construction of multinational Corporations is included a parent organization that holds all licensed innovation rights, contract makers or undeniable makers which complete the creative work in the nations with low work costs, and the restricted or undeniable wholesalers that sell the completed merchandise in different business sectors and behaviors market explores in their nations of activity. The pandemic effect on financial movement contrasts as far as extent and force. This has delivered unrivaled proportions of shortcomings. This paper tries to diagram the overall elements of worldwide Financial Management of MNCs (Multinational Corporations) with country explicit dangers and primary dangers. Fundamental data is accumulated from different online media, adroit journals, various kinds of examination papers, and other online sources, etc. The globalization of Financial Management of MNCs (Multinational Corporations) is coordinated to the enormous extension of greatness and enhancement of monetary exchanges. At long last, all areas show and portray in detail this subject and give a few suggestions to conquer the present circumstance.
 JEL Classification Codes: F36, G32, F23.

Infrastructure performance prediction under Climate-Induced Disasters using data analytics

The frequency of Climate-induced Disasters (CID) has tripled in the last three decades, driving the World Economic Forum to identify them as the most likely and most impactful risks worldwide. With more than 70% of the world population expected to be living in cities by 2050, ensuring the resilience of urban infrastructure systems under CID is crucial. The present work employs data analytics and machine learning techniques to develop a performance prediction framework for infrastructure systems under CID. The framework encompasses four stages related to: extracting meaningful information about the impact of CID on infrastructure systems and identifying the latter's performance; investigating the relationship between different CID attributes and previously identified system performance; employing data imputation using unsupervised machine learning techniques; and developing and testing a supervised machine learning model based on the different influencing CID attributes. To demonstrate its application, the developed framework is applied to disaster data compiled by the National Weather Services between 1996 and 2019 in the state of New York. The analysis results showed that: i) power systems in New York are the most vulnerable infrastructure to CID, and particularly to wind-related hazards; ii) power system performance level depends on hazard-system interactions rather than solely hazard characteristics; and iii) a 4-predictors random forest-based model can effectively predict power system performance with an accuracy of 89%. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.

Spatiotemporal Planning of Construction Projects: A Literature Review and Assessment of the State of the Art

Recently, new methods and technologies, including building information modeling (BIM) and lean management, have been introduced to the construction industry. Their objectives are to increase collaboration, ensure smooth flows of information, improve productivity, reduce different types of waste, and stabilize production. These constraints have forced the industry to gradually change by applying more adapted operational processes and contractual models that focus on value and by integrating the design, construction and operation phases. Traditional scheduling methods based on activities modelling have become less adapted to this new reality. As a result, complementary concepts have emerged, particularly the Last Planner system, takt time, space planning, and spatiotemporal scheduling. This paper presents an extensive review and an assessment of the state of the art of the spatiotemporal planning of construction projects. The paper categorizes the research efforts, presents a timeline of the most significant scientific developments in this domain, and addresses their possible impacts on the construction industry.

Enable faster and smoother spatio-temporal trajectory planning for autonomous vehicles in constrained dynamic environment

Trajectory planning is of vital importance to decision-making for autonomous vehicles. Currently, there are three popular classes of cost-based trajectory planning methods: sampling-based, graph-search-based, and optimization-based. However, each of them has its own shortcomings, for example, high computational expense for sampling-based methods, low resolution for graph-search-based methods, and lack of global awareness for optimization-based methods. It leads to one of the challenges for trajectory planning for autonomous vehicles, which is improving planning efficiency while guaranteeing model feasibility. Therefore, this paper proposes a hybrid planning framework composed of two modules, which preserves the strength of both graph-search-based methods and optimization-based methods, thus enabling faster and smoother spatio-temporal trajectory planning in constrained dynamic environment. The proposed method first constructs spatio-temporal driving space based on directed acyclic graph and efficiently searches a spatio-temporal trajectory using the improved A* algorithm. Then taking the search result as reference, locally convex feasible driving area is designed and model predictive control is applied to further optimize the trajectory with a comprehensive consideration of vehicle kinematics and moving obstacles. Results simulated in four different scenarios all demonstrated feasible trajectories without emergency stop or abrupt steering change, which is kinematic-smooth to follow. Moreover, the average planning time was 31 ms, which only took 59.05%, 18.87%, and 0.69%, respectively, of that consumed by other state-of-the-art trajectory planning methods, namely, maximum interaction defensive policy, sampling-based method with iterative optimizations, and Graph-search-based method with Dynamic Programming.

A stochastic approximation approach to spatio-temporal anchorage planning with multiple objectives

Globalization and subsequent increase in seaborne trade have necessitated efficient planning and management of world’s anchorage areas. These areas serve as a temporary stay area for commercial vessels for various reasons such as waiting for passage or port, fuel services, and bad weather conditions. The research question we consider in this study is how to place these vessels inside a polygon-shaped anchorage area in a dynamic fashion as they arrive and depart, which seems to be the first of its kind in the literature. We specifically take into account the objectives of (1) anchorage area utilization, (2) risk of vessel collisions, and (3) fuel consumption performance. These three objectives define our objective function in a weighted sum scheme. We present a spatio-temporal methodology for this multi-objective anchorage planning problem where we use Monte Carlo simulations to measure the effect of any particular combination of planning metrics (measured in real time for an incoming vessel) on the objective function (measured in steady state). We resort to the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm for identifying the linear combination of the planning metrics that optimizes the objective function. We present computational experiments on a major Istanbul Straight anchorage, which is one of the busiest in the world, as well as synthetic anchorages. Our results indicate that our methodology significantly outperforms comparable algorithms in the literature for daily anchorage planning. For the Istanbul Straight anchorage, for instance, reduction in risk was 42% whereas reduction in fuel costs was 45% when compared the best of the current state-of-the-art methods. Our methodology can be utilized within a planning expert system that intelligently places incoming vessels inside the anchorage so as to optimize multiple strategic goals. Given the flexibility of our approach in terms of the planning objectives, it can easily be adapted to more general variants of multi-objective spatio-temporal planning problems where certain objects need to be dynamically placed inside two or even-three dimensional spaces in an intelligent manner.