Download or read book Comap A Synthetic Dataset for Collective Multi agent Perception of Autonomous Driving written by Y. Yuan and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets written by Braden Hurl and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this work is to increase the performance of autonomous vehicle 3D object detection using synthetic data. This work introduces the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large, detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic Light Detection and Ranging (LiDAR) data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. This work describes a novel LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with the PreSIL framework is entirely automatic and requires no human intervention of any kind. The effectiveness of the PreSIL dataset is demonstrated by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with the PreSIL dataset. The PreSIL dataset and generation code are available at https://tinyurl.com/y3tb9sxy Synthetic data also enables data generation which is genuinely hard to create in the real world. In the next major chapter of this thesis, a new synthethic dataset, the TruPercept dataset, is created with perceptual information from multiple viewpoints. A novel system is proposed for cooperative perception, perception including information from multiple viewpoints. The TruPercept model is presented. TruPercept integrates trust modelling for vehicular ad hoc networks (VANETs) with information from perception, with a focus on 3D object detection. A discussion is presented on how this might create a safer driving experience for fully autonomous vehicles. The TruPercept dataset is used to experimentally evaluate the TruPercept model against traditional local perception (single viewpoint) models. The TruPercept model is also contrasted with existing methods for trust modeling used in ad hoc network environments. This thesis also offers insights into how V2V communication for perception can be managed through trust modeling, aiming to improve object detection accuracy, across contexts with varying ease of observability. The TruPercept model and data are available at https://tinyurl.com/y2nwy52o.

Download or read book Computer Vision ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-11-10 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Autonomous Driving Perception written by Rui Fan and published by Springer Nature. This book was released on 2023-10-06 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.

Download or read book Learning to Drive written by David Michael Stavens and published by Stanford University. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.

Download or read book Robust Environmental Perception and Reliability Control for Intelligent Vehicles written by Huihui Pan and published by Springer Nature. This book was released on 2023-11-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.

Download or read book Deep Reinforcement Learning Methods for Autonomous Driving Safety and Interactivity written by Xiaobai Ma and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: To drive a vehicle fully autonomously, an intelligent system needs to be capable of having accurate perception and comprehensive understanding of the surroundings, making reasonable predictions of the progressing of the scenario, and executing safe, comfortable, as well as efficient control actions. Currently, these requirements are mostly fulfilled by the intelligence of human drivers. During past decades, with the development of machine learning and computer science, artificial intelligence starts to show better-than-human performance on more and more practical applications, while autonomous driving is still one of the most attractive and difficult unconquered challenges. This thesis studies the challenges of autonomous driving on its safety and interaction with the surrounding vehicles, and how deep reinforcement learning methods could help address these challenges. Reinforcement learning (RL) is an important paradigm of machine learning which focuses on learning sequential decision-making policies which interact with the task environment. Combining with deep neural networks, the recent development of deep reinforcement learning has shown promising results on control and decision-making tasks with high dimensional observations and complex strategies. The capability and achievements of deep reinforcement learning indicate a wide range of potential applications in autonomous driving. Focusing on autonomous driving safety and interactivity, this thesis presents novel contributions on topics including safe and robust reinforcement learning, reinforcement learning-based safety test, human driver modeling, and multi-agent reinforcement learning. This thesis begins with the study of deep reinforcement learning methods on autonomous driving safety, which is the most critical concern for all autonomous driving systems. We study the autonomous driving safety problem from two points of view: the first is the risk caused by the reinforcement learning control policies due to the mismatch between simulations and the real world; the second is the deep reinforcement learning-based safety test. In both problems, we explore the usage of adversary reinforcement learning agents on finding failures of the system with different focuses: on the first problem, the RL adversary is trained and applied at the learning stage of the control policy to guide it to learn more robust behaviors; on the second problem, the RL adversary is used at the test stage to find the most likely failures in the system. Different learning approaches are proposed and studied for the two problems. Another fundamental challenge for autonomous driving is the interaction between the autonomous vehicle and its surrounding vehicles, which requires accurate modeling of the behavior of surrounding drivers. In the second and third parts of the thesis, we study the surrounding driver modeling problem on three different levels: the action distribution level, the latent state level, and the reasoning level. On the action distribution level, we explore advanced policy representations for modeling the complex distribution of driver's control actions. On the latent state level, we study how to efficiently infer the latent states of surrounding drivers like their driving characteristics and intentions, and how it could be combined with the learning of autonomous driving decision-making policies. On the reasoning level, we investigate the reasoning process between multiple interacting agents and use this to build their behavior models through multi-agent reinforcement learning.

Download or read book Learning to Drive written by David Michael Stavens and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Every year, 1.2 million people die in automobile accidents and up to 50 million are injured. Many of these deaths are due to driver error and other preventable causes. Autonomous or highly aware cars have the potential to positively impact tens of millions of people. Building an autonomous car is not easy. Although the absolute number of traffic fatalities is tragically large, the failure rate of human driving is actually very small. A human driver makes a fatal mistake once in about 88 million miles. As a co-founding member of the Stanford Racing Team, we have built several relevant prototypes of autonomous cars. These include Stanley, the winner of the 2005 DARPA Grand Challenge and Junior, the car that took second place in the 2007 Urban Challenge. These prototypes demonstrate that autonomous vehicles can be successful in challenging environments. Nevertheless, reliable, cost-effective perception under uncertainty is a major challenge to the deployment of robotic cars in practice. This dissertation presents selected perception technologies for autonomous driving in the context of Stanford's autonomous cars. We consider speed selection in response to terrain conditions, smooth road finding, improved visual feature optimization, and cost effective car detection. Our work does not rely on manual engineering or even supervised machine learning. Rather, the car learns on its own, training itself without human teaching or labeling. We show this "self-supervised" learning often meets or exceeds traditional methods. Furthermore, we feel self-supervised learning is the only approach with the potential to provide the very low failure rates necessary to improve on human driving performance.

Download or read book Deep Multi Agent Reinforcement Learning for Autonomous Driving written by Sushrut Bhalla and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and back-propagation have been successfully used to perform centralized training with communication protocols among multiple agents in a cooperative Multi-Agent Deep Reinforcement Learning (MARL) environment. In this work, I present techniques for centralized training of MARL agents in large scale environments and compare my work against current state of the art techniques. This work uses model-free Deep Q-Network (DQN) as the baseline model and allows inter agent communication for cooperative policy learning. I present two novel, scalable and centralized MARL training techniques (MA-MeSN, MA-BoN), which are developed under the principle that the behavior policy and message/communication policies have different optimization criteria. Thus, this work presents models which separate the message learning module from the behavior policy learning module. As shown in the experiments, the separation of these modules helps in faster convergence in complex domains like autonomous driving simulators and achieves better results than the current techniques in literature. Subsequently, this work presents two novel techniques for achieving decentralized execution for the communication based cooperative policy. The first technique uses behavior cloning as a method of cloning an expert cooperative policy to a decentralized agent without message sharing. In the second method, the behavior policy is coupled with a memory module which is local to each model. This memory model is used by the independent agents to mimic the communication policies of other agents and thus generate an independent behavior policy. This decentralized approach has minimal effect on degradation of the overall cumulative reward achieved by the centralized policy. Using a fully decentralized approach allows us to address the challenges of noise and communication bottlenecks in real-time communication channels. In this work, I theoretically and empirically compare the centralized and decentralized training algorithms to current research in the field of MARL. As part of this thesis, I also developed a large scale multi-agent testing environment. It is a new OpenAI-Gym environment which can be used for large scale multi-agent research as it simulates multiple autonomous cars driving cooperatively on a highway in the presence of a bad actor. I compare the performance of the centralized algorithms to existing state-of-the-art algorithms, for ex, DIAL and IMS which are based on cumulative reward achieved per episode and other metrics. MA-MeSN and MA-BoN achieve a cumulative reward of at-least 263% higher than the reward achieved by the DIAL and IMS. I also present an ablation study of the scalability of MA-BoN and show that MA-MeSN and MA-BoN algorithms only exhibit a linear increase in inference time and number of trainable parameters compared to quadratic increase for DIAL.

Download or read book Uncertainty aware Spatiotemporal Perception for Autonomous Vehicles written by Mikhal Itkina and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles are set to revolutionize transportation in terms of safety and efficiency. However, autonomous systems still have challenges operating in complex human environments, such as an autonomous vehicle in a cluttered, dynamic urban setting. A key obstacle to deploying autonomous systems on the road is understanding, anticipating, and making inferences about human behaviors. Autonomous perception builds a general understanding of the environment for a robot. This includes making inferences about human behaviors in both space and time. Humans are difficult to model due to their vastly diverse behaviors and rapidly evolving objectives. Moreover, in cluttered settings, there are computational and visibility limitations. However, humans also possess desirable capabilities, such as their ability to generalize beyond their observed environment. Although learning-based systems have had success in recent years in modeling and imitating human behavior, efficiently capturing the data and model uncertainty for these systems remains an open problem. This thesis proposes algorithmic advances to uncertainty-aware autonomous perception systems in human environments. We make system-level contributions to spatiotemporal robot perception that reasons about human behavior, and foundational advancements in uncertainty-aware machine learning models for trajectory prediction. These contributions enable robotic systems to make uncertainty- and socially-aware spatiotemporal inferences about human behavior. Traditional robot perception is object-centric and modular, consisting of object detection, tracking, and trajectory prediction stages. These systems can fail prior to the prediction stage due to partial occlusions in the environment. We thus propose an alternative end-to-end paradigm for spatiotemporal environment prediction from a map-centric occupancy grid representation. Occupancy grids are robust to partial occlusions, can handle an arbitrary number of human agents in the scene, and do not require a priori information regarding the environment. We investigate the performance of computer vision techniques in this context and develop new mechanisms tailored to the task of spatiotemporal environment prediction. Spatially, robots also need to reason about fully occluded agents in their environment, which may occur due to sensor limitations or other agents on the road obstructing the field of view. Humans excel at extrapolating from their experiences by making inferences from observed social behaviors. We draw inspiration from human intuition to fill in portions of the robot's map that are not observable by traditional sensors. We infer occupancy in these occluded regions by learning a multimodal mapping from observed human driver behaviors to the environment ahead of them, thus treating people as sensors. Our system handles multiple observed agents to maximally inform the occupancy map around the robot. In order to safely integrate human behavior modeling into the robot autonomy stack, the perception system must efficiently account for uncertainty. Human behavior is often modeled using discrete latent spaces in learning-based models to capture the multimodality in the distribution. For example, in a trajectory prediction task, there may be multiple valid future predictions given a past trajectory. To accurately model this latent distribution, the latent space needs to be sufficiently large, leading to tractability concerns for downstream tasks, such as path planning. We address this issue by proposing a sparsification algorithm for discrete latent sample spaces that can be applied post hoc without sacrificing model performance. Our approach successfully balances multimodality and sparsity to achieve efficient data uncertainty estimation. Aside from modeling data uncertainty, learning-based autonomous systems must be aware of their model uncertainty or what they do not know. Flagging out-of-distribution or unknown scenarios encountered in the real world could be helpful to downstream autonomy stack components and to engineers for further system development. Although the machine learning community has been prolific in model uncertainty estimation for small benchmark problems, relatively little work has been done on estimating this uncertainty in complex, learning-based robotic systems. We propose efficiently learning the model uncertainty over an interpretable, low-dimensional latent space in the context of a trajectory prediction task. The algorithms presented in this thesis were validated on real-world autonomous driving data and baselined against state-of-the-art techniques. We show that drawing inspiration from human-level reasoning while modeling the associated uncertainty can inform environment understanding for autonomous perception systems. The contributions made in this thesis are a step towards uncertainty- and socially-aware autonomous systems that can function seamlessly in human environments.

Download or read book Multi Sensor Multi Object Tracking in Autonomous Vehicles written by Surya Kollazhi Manghat and published by . This book was released on 2019 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self driving cars becoming more popular nowadays, which transport with it's own intelligence and take appropriate actions at adequate time. Safety is the key factor in driving environment. A simple fail of action can cause many fatalities. Computer Vision has major part in achieving this, it help the autonomous vehicle to perceive the surroundings. Detection is a very popular technique in helping to capture the surrounding for an autonomous car. At the same time tracking also has important role in this by providing dynamic of detected objects. Autonomous cars combine a variety of sensors such as RADAR, LiDAR, sonar, GPS, odometry and inertial measurement units to perceive their surroundings. Driver-assistive technologies like Adaptive Cruise Control, Forward Collision Warning system (FCW) and Collision Mitigation by Breaking (CMbB) ensure safety while driving. Perceiving the information from environment include setting up sensors on the car. These sensors will collect the data it sees and this will be further processed for taking actions. The sensor system can be a single sensor or multiple sensor. Different sensors have different strengths and weaknesses which makes the combination of them important for technologies like Autonomous Driving. Each sensor will have a limit of accuracy on it's readings, so multi sensor system can help to overcome this defects. This thesis is an attempt to develop a multi sensor multi object tracking method to perceive the surrounding of the ego vehicle. When the Object detection gives information about the presence of objects in a frame, Object Tracking goes beyond simple observation to more useful action of monitoring objects. The experimental results conducted on KITTI dataset indicate that our proposed state estimation system for Multi Object Tracking works well in various challenging environments.

Download or read book Public Transport Planning with Smart Card Data written by Fumitaka Kurauchi and published by CRC Press. This book was released on 2017-02-17 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collecting fares through "smart cards" is becoming standard in most advanced public transport networks of major cities around the world. Travellers value their convenience and operators the reduced money handling fees. Electronic tickets also make it easier to integrate fare systems, to create complex time and space differentiated fare systems, and to provide incentives to specific target groups. A less-utilised benefit is the data collected through smart cards. Records, even if anonymous, provide for a much better understanding of passengers’ travel behaviour as current literature shows. This information can also be used for better service planning. Public Transport Planning with Smart Card Data handles three major topics: how passenger behaviour can be estimated using smart card data, how smart card data can be combined with other trip databases, and how the public transport service level can be better evaluated if smart card data is available. The book discusses theory as well as applications from cities around the world and will be of interest to researchers and practitioners alike who are interested in the state-of-the-art as well as future perspectives that smart card data will bring.

Download or read book Introduction to Computational Science written by Angela B. Shiflet and published by Princeton University Press. This book was released on 2014-03-30 with total page 857 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential introduction to computational science—now fully updated and expanded Computational science is an exciting new field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. This textbook provides students with a versatile and accessible introduction to the subject. It assumes only a background in high school algebra, enables instructors to follow tailored pathways through the material, and is the only textbook of its kind designed specifically for an introductory course in the computational science and engineering curriculum. While the text itself is generic, an accompanying website offers tutorials and files in a variety of software packages. This fully updated and expanded edition features two new chapters on agent-based simulations and modeling with matrices, ten new project modules, and an additional module on diffusion. Besides increased treatment of high-performance computing and its applications, the book also includes additional quick review questions with answers, exercises, and individual and team projects. The only introductory textbook of its kind—now fully updated and expanded Features two new chapters on agent-based simulations and modeling with matrices Increased coverage of high-performance computing and its applications Includes additional modules, review questions, exercises, and projects An online instructor's manual with exercise answers, selected project solutions, and a test bank and solutions (available only to professors) An online illustration package is available to professors

Download or read book Recent Developments in Data Science and Business Analytics written by Madjid Tavana and published by Springer. This book was released on 2018-03-27 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains.

Download or read book Proceedings of 3rd International Conference on Computing Informatics and Networks written by Ajith Abraham and published by Springer Nature. This book was released on 2021-03-14 with total page 659 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of high-quality peer-reviewed research papers presented in the Third International Conference on Computing Informatics and Networks (ICCIN 2020) organized by the Department of Computer Science and Engineering (CSE), Bhagwan Parshuram Institute of Technology (BPIT), Delhi, India, during 29–30 July 2020. The book discusses a wide variety of industrial, engineering and scientific applications of the emerging techniques. Researchers from academic and industry present their original work and exchange ideas, information, techniques and applications in the field of artificial intelligence, expert systems, software engineering, networking, machine learning, natural language processing and high-performance computing.

Download or read book Handbook of Transport Modelling written by David A. Hensher and published by Elsevier Science Limited. This book was released on 2007-09-14 with total page 826 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since 2000, there has been an exponential amount of research completed in the field of transport modelling thereby creating a need for an expanded and revised edition of this book. National transport models have taken on the new modelling methods and there have been theoretical and empirical advances in performance measurement. Coverage will include current demand methods, data issues, valuation, cost and performance, and updated traffic models. Supplementary case studies will illustrate how modelling can be applied to the study of the different transport modes and the infrastructures that support them.The second edition of this handbook will continue to be an essential reference for researchers and practitioners in the field. All contributions are by leading experts in their fields and there is extensive cross-referencing of subject matter. This book features expanded coverage on emerging trends and updated case studies. It addresses models for specific applications (i.e. parking, national traffic forecasting, public transport, urban freight movements, and logistics management).

Download or read book Geographic Information Systems GIS written by Dayna Nielson and published by Nova Science Pub Incorporated. This book was released on 2014-01-01 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainability has been increasingly embraced as an overarching policy goal, and communities have been called to be active participants on the path towards attaining a balance between fundamental human needs and ecological resilience. Community-based organizations (CBOs) can benefit from using GIS in building community assets and developing well-conceived sustainability initiatives, but GIS has not yet been widely used for those purposes in CBOs. This book illustrates how geographic information (such as maps) can be useful in community development drawing from service-learning GIS projects, and argue that economic theories of sustainability and spatial thinking can be of help in building sustainable community. It also discusses the application of vehicle routing problems for sustainable waste collection; spatio-temporal visualization and analysis techniques in GIS; GIS applications in modern crop protection; role of geographic information system for water quality evaluation; and the use of remote sensing and GIS for groundwater potential mapping in crystalline basement rocks.