Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Advanced Driving Assistance Prediction Systems

Future automobiles are going to experience a fundamental evolution by installing semiotic predictor driver assistance equipment. To meet these equipment, Continuous driving-behavioral data have to be observed and processed to construct powerful predictive driving assistants. In this thesis, we focus on raw driving-behavioral data and present a prediction method which is able to prognosticate the next driving-behavioral state. This method has been constructed based on the unsupervised double articulation analyzer method (DAA) which is able to segment meaningless continuous driving-behavioral data into a meaningful sequence of driving situations. Thereafter, our novel model by mining the sequences of driving situations can define and process the most influential data parameters. After that, our model by utilizing these parameters can interpret the dynamic driving data and predict the next state of the determined vehicle. Proficiency of this model has been evaluated using over three terabytes driving behavioral data which include 16 drivers’ data, totally for more than 17 hours and over 456 Km

Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals. For this purpose, we propose an integrative generative model that combines a language model and an acoustic model into a single generative model called the "hierarchical Dirichlet process hidden language model" (HDP-HLM). The HDP-HLM is obtained by extending the hierarchical Dirichlet process hidden semi-Markov model (HDP-HSMM) proposed by Johnson et al. An inference procedure for the HDP-HLM is derived using the blocked Gibbs sampler originally proposed for the HDP-HSMM. This procedure enables the simultaneous and direct inference of language and acoustic models from continuous speech signals. Based on the HDP-HLM and its inference procedure, we developed a novel double articulation analyzer. By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically organized latent words and phonemes, of the data in an unsupervised manner. The novel unsupervised double articulation analyzer is called NPB-DAA. The NPB-DAA can automatically estimate double articulation structure embedded in speech signals. We also carried out two evaluation experiments using synthetic data and actual human continuous speech signals representing Japanese vowel sequences. In the word acquisition and phoneme categorization tasks, the NPB-DAA outperformed a conventional double articulation analyzer (DAA) and baseline automatic speech recognition system whose acoustic model was trained in a supervised manner.Comment: 15 pages, 7 figures, Draft submitted to IEEE Transactions on Autonomous Mental Development (TAMD

Context-aware Security for Vehicles and Fleets: A Survey

Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future

Fusion of Data from Heterogeneous Sensors with Distributed Fields of View and Situation Evaluation for Advanced Driver Assistance Systems

In order to develop a driver assistance system for pedestrian protection, pedestrians in the environment of a truck are detected by radars and a camera and are tracked across distributed fields of view using a Joint Integrated Probabilistic Data Association filter. A robust approach for prediction of the system vehicles trajectory is presented. It serves the computation of a probabilistic collision risk based on reachable sets where different sources of uncertainty are taken into account

Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students

In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies. To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on). The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts. This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution. The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials. The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world

Interpreting complex scenes using a hierarchy of prototypical scene models

Bonnin S. Interpreting complex scenes using a hierarchy of prototypical scene models. Bielefeld: Universitätsbibliothek Bielefeld; 2015.To drive safely, a good driver observes her surroundings, anticipates the actions of other traffic participants and then decides for a maneuver. But if a driver is inattentive or overloaded, she may fail to include some relevant information. This can then lead to wrong decisions and potentially result in an accident. In order to assist a driver in her decision making, Advanced Driver Assistance Systems (ADAS) are becoming more and more popular in commercial cars. The quality of these existing systems compared to an experienced driver is relatively low, because they purely rely on physical observation and thus react only shortly before an accident. To fully avoid a collision, a driver needs more time to react, therefore the driver should receive an early warning. For an earlier warning of the driver, behaviors of other traffic participants would have to be predicted. We classify existing research in this area with respect to two aspects: quality and scope. Quality means the ability to warn a driver early before a dangerous situation. Scope means the diversity of scenes in which the approach can work. In general we see two tendencies, methods targeting for broad scope but having low quality and those targeting for narrow scope but high quality. Our goal is to have a system with high quality and wide scope. To achieve this, we propose a generic framework, called Context Model Tree (CMT), that combines multiple high quality classifiers to predict if an entity is coming into the way of the ego-vehicle for many scenarios. This framework is a tree structure in which context based models are ordered according to their context specificity, from the generic ones in the top nodes to the most specific ones in the leaves. We have designed a set of activation rules to activate the nodes fitting to the current situation, using sensory information like GPS, digital maps or vision. To show that a combination of general and specific classifiers is a solution to improve quality and scope, this thesis introduces the generic concept of our system followed by a concrete implementation for predicting if an entity is coming into the way of the ego-vehicle when changing lane for highway scenarios. On the highway, a driver usually changes lane for a reason. Our models use complex features based on contextual information and relations between entities. On the highway, one of the most influential indicators to predict if a vehicle is going to change lane is a slow predecessor. A CMT for highway contains in the top node a model that uses such general indicators. Two models to predict lane changes at entrance and giveway lanes are placed as sub-nodes. These models make use of the specific information inherent to these contexts. We will provide a comparison of the quality of the three models separately and the combination of the models using a CMT and show that, in general, the CMT performs better in terms of prediction time horizon and prediction errors. In order to show the flexibility and adaptability of the CMT, we also present an extension of the framework for pedestrian crossing prediction in inner-city scenarios. In inner-city, a pedestrian who wants to cross a road without having the priority to do so and decide not to is usually influenced by its surroundings, for example a vehicle approaching too fast and not having enough time to cross. A CMT for inner-city contains in the top node a model that uses such general indicators to predict crossing behaviors at an early time for any road, in particular roads where pedestrians do not have the priority to cross. However, there are specific locations such as zebra crossings, where based on expert driving experience, one would expect that a prediction can be done even earlier. Therefore, we have developed an additional specific model fitted to the context of zebra crossings. This model makes use of the specific information inherent to this context. The experiments show that this model produces both, better and earlier predictions in this specific context. Because our goal is to build a generic behavior prediction system, we finally apply the framework of the CMT to combine the two models. We demonstrate that this multi-model system is well suited to provide early predictions for realistic data, including both, generic inner-city situations and zebra crossings. This work could therefore be a step towards better advanced Driver Assistance Systems (ADAS), through the generation of earlier warnings to increase the reaction time of a driver