Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Korthals T, Kragh M, Christiansen P, Karstoft H, Jørgensen RN, Rückert U. Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 2018;5: 26.Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes

Distance,Time and Terms in First Story Detection

First Story Detection (FSD) is an important application of online novelty detection within Natural Language Processing (NLP). Given a stream of documents, or stories, about news events in a chronological order, the goal of FSD is to identify the very first story for each event. While a variety of NLP techniques have been applied to the task, FSD remains challenging because it is still not clear what is the most crucial factor in defining the “story novelty”. Giventhesechallenges,thethesisaddressedinthisdissertationisthat the notion of novelty in FSD is multi-dimensional. To address this, the work presented has adopted a three dimensional analysis of the relative qualities of FSD systems and gone on to propose a specific method that wearguesignificantlyimprovesunderstandingandperformanceofFSD. FSD is of course not a new problem type; therefore, our first dimen sion of analysis consists of a systematic study of detection models for firststorydetectionandthedistancesthatareusedinthedetectionmod els for defining novelty. This analysis presents a tripartite categorisa tion of the detection models based on the end points of the distance calculation. The study also considers issues of document representation explicitly, and shows that even in a world driven by distributed repres iv entations,thenearestneighbourdetectionmodelwithTF-IDFdocument representations still achieves the state-of-the-art performance for FSD. Weprovideanalysisofthisimportantresultandsuggestpotentialcauses and consequences. Events are introduced and change at a relatively slow rate relative to the frequency at which words come in and out of usage on a docu ment by document basis. Therefore we argue that the second dimen sion of analysis should focus on the temporal aspects of FSD. Here we are concerned with not only the temporal nature of the detection pro cess, e.g., the time/history window over the stories in the data stream, but also the processes that underpin the representational updates that underpin FSD. Through a systematic investigation of static representa tions, and also dynamic representations with both low and high update frequencies, we show that while a dynamic model unsurprisingly out performs static models, the dynamic model in fact stops improving but stays steady when the update frequency gets higher than a threshold. Our third dimension of analysis moves across to the particulars of lexicalcontent,andcriticallytheaffectoftermsinthedefinitionofstory novelty. Weprovideaspecificanalysisofhowtermsarerepresentedfor FSD, including the distinction between static and dynamic document representations, and the affect of out-of-vocabulary terms and the spe cificity of a word in the calculation of the distance. Our investigation showed that term distributional similarity rather than scale of common v terms across the background and target corpora is the most important factor in selecting background corpora for document representations in FSD. More crucially, in this work the simple idea of the new terms emerged as a vital factor in defining novelty for the first story

TECHNOLOGY ASSESSMENT FOR SUSTAINABILITY IN WATER USE. OPERATIONALIZATION OF A RESPONSIBLE GOVERNANCE BASED IN RESPONSIBLE RESEARCH AND INNOVATION (ANTICIPATION AND INCLUSIVENESS)

The management of sustainability in water resources has underscored the critical importance of determining appropriate decision-making processes and establishing effective governance structures. Gaining comprehensive insights into the decision-making mechanisms and actors involved is pivotal for tackling present as well as prospective issues related to water efficiently. This research evaluates the interplay among water scarcity, responsible technologies for water use, and systems of governance for sustainability amid swift technological progress. Furthermore, it delves into the congruity of said endeavors with the Sustainable Development Goals (SDGs), other sustainability water frameworks and the social and political ecosystem. In this context, the active engagement and participation of societal actors, and not only stakeholders, assume a pivotal role as it significantly impacts the decision-making processes and molds the results of sustainability initiatives. An innovative approach to the concepts of responsibility and sustainability is predicated on the quality of the relationship between the network of societal actors as a key point. This work underscores the importance of establishing strong and comprehensive relationships to address the challenges concerning water management and promote the adoption of sustainable approaches, in co-creation, not only of knowledge but the epistemic subject in the process. This work sheds light on the interrelated domains of water management, sustainability, and regulation. A novel proposal is presented via a simulation exercise and use the socio-technical framework for the purpose of fostering responsible water use. The comprehension and use of responsible technology and innovation in the realm of water u management will be enhanced through the technique of operationalizing open anticipatory governance and executing a simulated experiment. By using a digital deliberation space and establishing a systematic approach towards technology assessment and sustainability, using the relational quality of the network of actors as the key element for co-production of knowledge, science and technology, the present study has produced and materialized an innovative framework.Na sustentabilidade da gestão da água reveste-se de especial importância determinar processos de tomada de decisão adequados e estabelecer estruturas de governação eficazes. Obter uma visão abrangente sobre os mecanismos de tomada de decisão e os atores envolvidos é fundamental para abordar questões presentes e futuras relacionadas ao uso eficiente da água. Este trabalho procura conhecer a interação entre gestão de água, tecnologias responsáveis pelo uso da água e sistemas de governança para a sustentabilidade. Adicionalmente, pretende conhecer a relação com os Sustainable Development Goals (SDGs), outros programas de sustentabilidade, bem como com o ecossistema social e político. Neste contexto, o envolvimento e a participação ativa dos atores sociais, e não apenas de stakeholders, assume um papel fundamental, uma vez que, não só, impactam significativamente os processos de tomada de decisão, mas, também, moldam os resultados das iniciativas de sustentabilidade. Nesta nova aproximação ao conceito de responsabilidade e sustentabilidade encontra-se a qualidade da relação entre a rede de atores sociais como ponto-chave. Sublinha-se a importância de estabelecer uma qualidade relacional enriquecida e abrangente para enfrentar de forma mais estruturada os desafios relativos à gestão da água de forma eficiente e promover a adoção de abordagens sustentáveis. Com este trabalho, procura-se aprofundar os domínios inter-relacionados da gestão da água, sustentabilidade e regulamentação. É elaborada uma proposta de simulação, utilizando uma perspetiva sociotécnica com o objetivo de capacitar a co-constituição como sujeitos e a compreensão e utilização de tecnologia responsável e inovação no âmbito da gestão do uso da água utilizando operacionalização da governação antecipatória aberta. O presente estudo materializa seu carácter de inovação ao utilizar um espaço de deliberação digital e ao estabelecer uma abordagem sistemática para a avaliação da tecnologia e sustentabilidade, usando a qualidade relacional da rede de atores como elemento-chave para a coprodução de conhecimento, ciência e tecnologia e co-constituição do próprio sujeito no processo de deliberação

Cybersecurity, Identity Theft, and the Limits of Tort Liability

Tort law is the best vehicle for allocating the risks and spreading the costs of database intrusion. It can incentivize database possessors (“possessors”) and data subjects to minimize the harm associated with breaches of database security while also balancing each party’s interests. Life is built upon computerized databases and the information of those databases is subject to hackers and other cyber-threats, which can cause catastrophic damage. It is hard to identify hackers; however, a better object for recovery is likely the possessors who fail to prevent or reveal a security breach. The law governing database possessors’ liability is far from settled; however, possessors have a duty to: (1) protect the information stored; and (2) disclose evidence of breaches. These duties can arise out of statute, principles of tort law, or the fiduciary duty doctrine, with each of the source shaping the duty imposed on possessors and the scope of their potential liability. The scope of database possessor liability is judiciously limited by the Economic Loss Rule, limiting the scope of potential liability vested upon possessors to Security-Monitoring costs, a remedy that balances the interests of data possessors and subjects. Tort principles, when carefully applied to contemporary cybersecurity, can perform a key role in protecting the economic foundations of modern life

Cybersecurity, Identity Theft, and the Limits of Tort Liability

Tort law is the best vehicle for allocating the risks and spreading the costs of database intrusion. It can incentivize database possessors (“possessors”) and data subjects to minimize the harm associated with breaches of database security while also balancing each party’s interests. Life is built upon computerized databases and the information of those databases is subject to hackers and other cyber-threats, which can cause catastrophic damage. It is hard to identify hackers; however, a better object for recovery is likely the possessors who fail to prevent or reveal a security breach. The law governing database possessors’ liability is far from settled; however, possessors have a duty to: (1) protect the information stored; and (2) disclose evidence of breaches. These duties can arise out of statute, principles of tort law, or the fiduciary duty doctrine, with each of the source shaping the duty imposed on possessors and the scope of their potential liability. The scope of database possessor liability is judiciously limited by the Economic Loss Rule, limiting the scope of potential liability vested upon possessors to Security-Monitoring costs, a remedy that balances the interests of data possessors and subjects. Tort principles, when carefully applied to contemporary cybersecurity, can perform a key role in protecting the economic foundations of modern life

Cybersecurity, Identity Theft, and the Limits of Tort Liability

This article considers to what extent database possessors (such as credit card companies and universities) can be held liable for harm caused to data subjects (such as consumers, applicants, and alumni) when information relating to those persons is hacked or otherwise subject to improper access. Addressing common-law and statutory sources (including new legislation in 17 states) the article clearly differentiates the duty to safeguard data from the duty to notify data subjects that the security of their information has been breached. By analogy to the “medical-monitoring damages” which some states award in toxic-exposure cases, the article argues that “security-monitoring damages” should be available in database-intrusion cases. More specifically, the article proposes that, in cases of ordinary negligence, the interests of society will be best served by limiting recoverable economics losses to the cost of security-monitoring damages once a database possessor discloses to the affected individual the fact that data has been improperly accessed. This approach will encourage database possessors to discover and reveal instances of data intrusion. It will also place data subjects in a position to protect their own interests by monitoring their economic and personal security when there is heightened vulnerability

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality