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Compiled from papers of the 4th Biennial Workshop on DSP (Digital Signal Processing) for In-Vehicle Systems and Safety this edited collection features world-class experts from diverse fields focusing on integrating smart in-vehicle systems with human factors to enhance safety in automobiles. Digital Signal Processing for In-Vehicle Systems and Safety presents new approaches on how to reduce driver inattention and prevent road accidents. The material addresses DSP technologies in adaptive automobiles, in-vehicle dialogue systems, human machine interfaces, video and audio processing, and in-vehicle speech systems. The volume also features recent advances in Smart-Car technology, coverage of autonomous vehicles that drive themselves, and information on multi-sensor fusion for driver ID and robust driver monitoring. Digital Signal Processing for In-Vehicle Systems and Safety is useful for engineering researchers, students, automotive manufacturers, government foundations and engineers working in the areas of control engineering, signal processing, audio-video processing, bio-mechanics, human factors and transportation engineering.
Building effective and user-friendly transportation systems is one of the big challenges for engineers in the 21st century. There is an increasing need to understand, model, and govern such systems at both, the individual and the society level. Traffic and transportation scenarios are extraordinarily appealing for Distributed Artificial Intelligence, and (multi-)agent technology in particular. This book gives an overview of recent advances in agent-based transportation systems.
The Federal Highway Administration (FHWA) Office of Operations Research and Development, located at Turner-Fairbank Highway Research Center (TFHRC), added five new research vehicles to FHWA's Innovation Research Vehicle Fleet. This fleet offers an experimental connected automation research platform that provides advanced capabilities for future operational concepts and supports their evaluation. In addition, the fleet's research platform enables full automatic control of longitudinal movements (such as acceleration and braking) with the flexibility to support lateral control (such as steering controls) for future autonomous vehicle research. Cooperative Adaptive Cruise Control (CACC) is the first operational implementation developed and tested on the new research platform. The CACC implementation will provide the ability to test the open architecture of the vehicle fleet technology platform and assess the ability of researchers to use the vehicle fleet to support the study of operational concepts and connected automation applications.
Agent Technology, or Agent-Based Approaches, is a new paradigm for developing software applications. It has been hailed as 'the next significant breakthrough in software development', and 'the new revolution in software' after object technology or object-oriented programming. In this context, an agent is a computer system which is capable of acting autonomously in its environment in order to meet its design objectives. So in the area of concurrent design and manufacturing, a manufacturing resource, namely a machine or an operator, may cooperate and negotiate with other agents for task assignment; and an existing engineering software can be integrated with a distributed integrated engineering design and manufacturing system. Hence in agent-based systems, there is no centralized system control structure, and no pre-defined agenda for the system execution, as exist in traditional systems. This book systematically describes the principles, key issues, and applications of agent technology in relation to concurrent engineering design and manufacturing. It introduces the methodology, standards, frameworks, tools, and languages of agent-based approaches and presents a general procedure for building agent-based concurrent engineering design and manufacturing systems. Both professional and university researchers and postgraduates should find this an invaluable presentation of the corresponding theories and methods, with some practical examples for developing multi-agent systems in the domain.
Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves.
This document examines a number of theoretical approaches suitable for creating an intelligent, adaptive interface for control of uninhabited aerial vehicles (UAVs) and uninhabited combat aerial vehicles (UCAVs). These theoretical elements have been combined to produce a comprehensive, cross-disciplinary approach to intelligent system design & implementation, or a generic framework. The framework is generic in that it is designed to be applicable to many other complex military systems with high operator workloads. The proposed framework uses an intelligent software agent-based approach in order to alleviate some of the demands on users performing highly complex tasks in difficult mission scenarios. The document presents overviews of the individual components of the framework and describes the contribution of each along with the rationale for their inclusion in the integrated approach. The generic framework is composed of elements from design approaches including knowledge-based systems, explicit models design, perceptual control theory, and ecological interface design. The final section indicates the recommended sequence for applying the framework.
Traffic congestion is a serious problem in the USA that affects safety, economy, environments, and human lives. Autonomous vehicles (AVs) equipped with vehicle-to-everything (V2X) communication technology is emerging as a viable solution to mitigate traffic congestion. In this dissertation, we propose an advanced traffic control system for autonomous vehicles, utilizing machine learning techniques, to alleviate traffic congestion, and enhance traffic efficiency and safety. The proposed system consists of two key components: an intelligent adaptive cruise control system (ACC) and a cooperative lane-change system. To address the limitations of existing static model-based approaches, we introduce a novel AI-based ACC system that dynamically adjusts the ACC settings based on real-time traffic conditions. By adapting to changing situations, this system significantly improves traffic efficiency. However, we recognize that current intelligent ACC systems primarily focus on traffic flow enhancement, disregarding the influence of adaptive inter-vehicle gap adjustment on driving safety and comfort. To bridge this gap, we develop a Safety-Aware Intelligent ACC system, which effectively assesses driving safety by dynamically updating safety model parameters according to varying traffic conditions. This innovative approach ensures that driving safety and comfort are prioritized alongside traffic efficiency. Furthermore, we present a novel multi-agent reinforcement learning (MARL)-based intelligent lane-change system for autonomous vehicles. This system optimizes both local and global performance by incorporating a road-side unit (RSU) responsible for managing a specific road segment, as well as vehicle-to-everything (V2X) capabilities for the agents. This density-aware cooperative multi-agent framework enables efficient and safe lane changes, considering the overall traffic conditions and maximizing the benefits for all vehicles involved. Finally, we present a use case scenario of our proposed next-generation traffic control system by designing an intelligent adaptive motion control system for electric vehicles (EVs) which facilitates an EV to control its motion to align with the position where the electromagnetic strength is expected to be maximal to receive maximum charging efficiency. By combining the AI-based ACC system and the MARL-based intelligent lane-change system, our next-generation traffic control system for autonomous vehicles aims to revolutionize traffic management, offering improved efficiency and safety for autonomous vehicles on the roads of the future.
This project was dedicated to the development of multi-agent systems with nontrivial capabilities for adaptation and control in complex operational environments. The agents in such systems are based on control structures and processes that are inherently purpose-driven. The basic building blocks underlying the operation of purpose-driven agent systems are Elementary Adaptive Modules. EAMs have six elements: a purpose, an action, an action target, an activation, an evaluation function, and a set of control variables. EAMs can be passive or active. Passive EAMs, like servo-mechanisms, act in response to stimuli that are triggered in their external environment. Active EAMs, like hill-climbers, initiate probes in the outside world, evaluate the response, and change their behavior accordingly. EAM-based agents are cascaded to form multi-level systems whose control structures generate goal-oriented behaviors with greatly enhanced capabilities for adaptation and learning.