1,178 research outputs found

    Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)

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    Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces potential for overfit to latent rules and features. Automating worst-case scenario generation could yield informative data for improving self driving. To this end, we introduce a "Physically Adversarial Intelligent Network" (PAIN), wherein self-driving vehicles interact aggressively in the CARLA simulation environment. We train two agents, a protagonist and an adversary, using dueling double deep Q networks (DDDQNs) with prioritized experience replay. The coupled networks alternately seek-to-collide and to avoid collisions such that the "defensive" avoidance algorithm increases the mean-time-to-failure and distance traveled under non-hostile operating conditions. The trained protagonist becomes more resilient to environmental uncertainty and less prone to corner case failures resulting in collisions than the agent trained without an adversary

    Annotated Bibliography: Anticipation

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    Compensation at the Crossroads: Autonomous Vehicles & Alternative Victim Compensation Schemes

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    Fully autonomous vehicles will become available to consumers within the next five to seven years. Experts predict that these vehicles will be drastically safer than their human-driven counterparts and will save thousands of lives each year in the United States alone. However, crashes will still occur, and when they do, they will raise unique and troubling issues about liability and fault that both negligence and products liability jurisprudence are not yet wellsuited to handle. Whether the civil justice system can adjudicate autonomous vehicle crash cases fairly and efficiently impacts (a) whether manufacturers can afford to produce these vehicles or whether the cost and magnitude of litigation surrounding them will destroy their market, (b) whether consumers will adopt this new technology, and (c) the rate at which they will be willing and able to do so. These issues, in turn, have an impact on how many lives can be saved on U.S. roads each year. It is thus imperative to design a method of compensating victims, protecting manufacturers, and giving courts time and space to develop jurisprudence applicable to this technology if we wish to reap the profound benefits that fully autonomous vehicles stand to offer. Although filing a lawsuit in the civil justice system will always be an option for victims of autonomous vehicle crashes, a specially designed, no-fault victim compensation fund offers a sensible way to address the issues identified above and to resolve these cases in a faster and less costly manner. While the use of victim compensation funds is a fairly recent phenomenon in the United States, these funds have been used with great success in a variety of situations and could be used successfully here. In the model proposed in this paper, an autonomous vehicle crash victim compensation fund would be administered by the National Highway Traffic Safety Administration (NHTSA) and financed by a tax levied on the sale of all fully autonomous vehicles. Victims who wish to seek compensation from the fund would be able to do so via a simple claim form and an agreement to waive their right to sue. Manufacturers, in turn, would be required to participate in a datasharing and design improvement program as a condition of receiving protection from the fund. This program would both assist NHTSA in gathering the information it needs to regulate autonomous vehicles and reduce the likelihood that a victim compensation fund would undermine manufacturer incentives to improve the safety of their vehicles. Participation by both victims and manufacturers would be voluntary, but the benefits of entering the fund would likely induce high levels of participation from both

    Emotional Design: An Overview

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    Emotional design has been well recognized in the domain of human factors and ergonomics. In this chapter, we reviewed related models and methods of emotional design. We are motivated to encourage emotional designers to take multiple perspectives when examining these models and methods. Then we proposed a systematic process for emotional design, including affective-cognitive needs elicitation, affective-cognitive needs analysis, and affective-cognitive needs fulfillment to support emotional design. Within each step, we provided an updated review of the representative methods to support and offer further guidance on emotional design. We hope researchers and industrial practitioners can take a systematic approach to consider each step in the framework with care. Finally, the speculations on the challenges and future directions can potentially help researchers across different fields to further advance emotional design.http://deepblue.lib.umich.edu/bitstream/2027.42/163319/1/Emotional_Design_Manuscript_Final.pdfSEL

    Object detection, distributed cloud computing and parallelization techniques for autonomous driving systems.

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    Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks

    Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

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    There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

    Programming tools for intelligent systems

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    Les outils de programmation sont des programmes informatiques qui aident les humains à programmer des ordinateurs. Les outils sont de toutes formes et tailles, par exemple les éditeurs, les compilateurs, les débogueurs et les profileurs. Chacun de ces outils facilite une tâche principale dans le flux de travail de programmation qui consomme des ressources cognitives lorsqu’il est effectué manuellement. Dans cette thèse, nous explorons plusieurs outils qui facilitent le processus de construction de systèmes intelligents et qui réduisent l’effort cognitif requis pour concevoir, développer, tester et déployer des systèmes logiciels intelligents. Tout d’abord, nous introduisons un environnement de développement intégré (EDI) pour la programmation d’applications Robot Operating System (ROS), appelé Hatchery (Chapter 2). Deuxièmement, nous décrivons Kotlin∇, un système de langage et de type pour la programmation différenciable, un paradigme émergent dans l’apprentissage automatique (Chapter 3). Troisièmement, nous proposons un nouvel algorithme pour tester automatiquement les programmes différenciables, en nous inspirant des techniques de tests contradictoires et métamorphiques (Chapter 4), et démontrons son efficacité empirique dans le cadre de la régression. Quatrièmement, nous explorons une infrastructure de conteneurs basée sur Docker, qui permet un déploiement reproductible des applications ROS sur la plateforme Duckietown (Chapter 5). Enfin, nous réfléchissons à l’état actuel des outils de programmation pour ces applications et spéculons à quoi pourrait ressembler la programmation de systèmes intelligents à l’avenir (Chapter 6).Programming tools are computer programs which help humans program computers. Tools come in all shapes and forms, from editors and compilers to debuggers and profilers. Each of these tools facilitates a core task in the programming workflow which consumes cognitive resources when performed manually. In this thesis, we explore several tools that facilitate the process of building intelligent systems, and which reduce the cognitive effort required to design, develop, test and deploy intelligent software systems. First, we introduce an integrated development environment (IDE) for programming Robot Operating System (ROS) applications, called Hatchery (Chapter 2). Second, we describe Kotlin∇, a language and type system for differentiable programming, an emerging paradigm in machine learning (Chapter 3). Third, we propose a new algorithm for automatically testing differentiable programs, drawing inspiration from techniques in adversarial and metamorphic testing (Chapter 4), and demonstrate its empirical efficiency in the regression setting. Fourth, we explore a container infrastructure based on Docker, which enables reproducible deployment of ROS applications on the Duckietown platform (Chapter 5). Finally, we reflect on the current state of programming tools for these applications and speculate what intelligent systems programming might look like in the future (Chapter 6)

    Attack on the Brain: Neurowars and Neurowarfare

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    Is neurotechnology leading nation-states toward a new domain of war? Neuroscience is on the verge of deciphering the human brain. As a result, brains will become a part of the battlefield against which attacks will be directed. As neuroscientist James Giordano argued: “the brain is the next battlespace.” It is foreseeable that this will have tremendous implications for warfare and could amount to a true military revolution in the sense of military historian Williamson Murray: it would completely change the characteristics of conflict, as well as transform state and society
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