Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whether or not others are cooperating. This work aims to extend the capabilities of automated vehicles, enabling them to cooperate implicitly in heterogeneous environments. Continuous actions allow for arbitrary trajectories and hence are applicable to a much wider class of problems than existing cooperative approaches with discrete action spaces. Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS) in conjunction with Decoupled-UCT evaluates the action-values of each agent in a cooperative and decentralized way, respecting the interdependence of actions among traffic participants. The extension to continuous action spaces is addressed by incorporating novel MCTS-specific enhancements for efficient search space exploration. The proposed algorithm is evaluated under different scenarios, showing that the algorithm is able to achieve effective cooperative planning and generate solutions egocentric planning fails to identify

Linear Progressive Coding for Semantic Communication using Deep Neural Networks

We propose a general method for semantic representation of images and other data using progressive coding. Semantic coding allows for specific pieces of information to be selectively encoded into a set of measurements that can be highly compressed compared to the size of the original raw data. We consider a hierarchical method of coding where a partial amount of semantic information is first encoded a into a coarse representation of the data, which is then refined by additional encodings that add additional semantic information. Such hierarchical coding is especially well-suited for semantic communication i.e. transferring semantic information over noisy channels. Our proposed method can be considered as a generalization of both progressive image compression and source coding for semantic communication. We present results from experiments on the MNIST and CIFAR-10 datasets that show that progressive semantic coding can provide timely previews of semantic information with a small number of initial measurements while achieving overall accuracy and efficiency comparable to non-progressive methods

NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials

Recent advances in deep learning have led to increased interest in solving high-efficiency end-to-end transmission problems using methods that employ the nonlinear property of neural networks. These techniques, we call neural joint source-channel coding (NeurJSCC), extract latent semantic features of the source signal across space and time, and design corresponding variable-length NeurJSCC approaches to transmit latent features over wireless communication channels. Rapid progress has led to numerous research papers, but a consolidation of the discovered knowledge has not yet emerged. In this article, we gather diverse ideas to categorize the expansive aspects on NeurJSCC as two paradigms, i.e., explicit and implicit NeurJSCC. We first focus on those two paradigms of NeurJSCC by identifying their common and different components in building end-to-end communication systems. We then focus on typical applications of NeurJSCC to various communication tasks. Our article highlights the improved quality, flexibility, and capability brought by NeurJSCC, and we also point out future directions

Cybertopia, Dystopia Or More Of The Same—Recent Writings On The Unknowable Future Of The Internet.

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Generative Joint Source-Channel Coding for Semantic Image Transmission

Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission. However, these methods mostly focus on the distortion of the reconstructed signals with respect to the input image, rather than their perception by humans. However, focusing on traditional distortion metrics alone does not necessarily result in high perceptual quality, especially in extreme physical conditions, such as very low bandwidth compression ratio (BCR) and low signal-to-noise ratio (SNR) regimes. In this work, we propose two novel JSCC schemes that leverage the perceptual quality of deep generative models (DGMs) for wireless image transmission, namely InverseJSCC and GenerativeJSCC. While the former is an inverse problem approach to DeepJSCC, the latter is an end-to-end optimized JSCC scheme. In both, we optimize a weighted sum of mean squared error (MSE) and learned perceptual image patch similarity (LPIPS) losses, which capture more semantic similarities than other distortion metrics. InverseJSCC performs denoising on the distorted reconstructions of a DeepJSCC model by solving an inverse optimization problem using style-based generative adversarial network (StyleGAN). Our simulation results show that InverseJSCC significantly improves the state-of-the-art (SotA) DeepJSCC in terms of perceptual quality in edge cases. In GenerativeJSCC, we carry out end-to-end training of an encoder and a StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms DeepJSCC both in terms of distortion and perceptual quality.Comment: 12 pages, 9 figure

Forth Industrial Revolution (4 IR) : digital disruption of cyber-physical systems

Article focus of the disruptive character of technological innovations brought by Fourth Industrial Revolution (4IR), withits unprecedented scale and scope, and exponential speed of incoming innovations, described from the point view of 'unintended consequences' (cross cutting impact of disruptive technologies across many sectors and aspects of human life). With integration of technology innovations emerging in number of fields including advanced robotics, pervasive computing, artificial intelligence, nano-and bio-technologies, additive and smart manufacturing, Forth Industrial Revolution introduce new ways in which technology becomes embedded not only within the society, economy and culture, but also within human body and mind (described by integration of technologies, collectively referred to as cyber-physical systems). At the forefront of digital transformation, based on cyber physical systems, stands Industry 4.0, referring to recent technological advances, where internet and supporting technologies (embedded systems) are serving as framework to integrate physical objects, human actors, intelligent machines, production lines and processes across organizational boundaries to form new kind of intelligent, networked value chain, called smart factory. Article presents broader context of 'disruptive changes (innovations)' accompanying 4IR, that embrace both economical perspective of 'broaderrestructuring' of modern economy and society (described in second part of the article as transition from second to third and forth industrial revolution), and technological perspective of computer and informational science with advances in pervasive computing, algorithms and artificial intelligence (described in third part of article with different stages of web development : web 1.0, web 2.0, web 3.0, web 4.0). What's more important, article presents hardly ever described in literature, psychological and philosophical perspective, more or less subtle reconfiguration made under the influence of these technologies, determining physical (body), psychological (mind) and philosophical aspect of human existence (the very idea of what it means to be the human), fully depicted in the conclusion of the article. The core element (novelty) is the attempt to bring full understanding and acknowledgment of disruptive innovations', that "change not only of the what and the how things are done, but also the who we are", moving beyond economical or technological perspective, to embrace also psychological and philosophical one