Agents and Robots for Reliable Engineered Autonomy

This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

Tier 1 Highway Security Sensitive Material Dynamic Risk Management

Each year, over 2 billion tons of hazardous materials are shipped in the United States, with over half of that being moved on commercial vehicles. Given their relatively poor or nonexistent defenses and inconspicuousness, commercial vehicles transporting hazardous materials are an easy target for terrorists. Before carriers or security agencies recognize that something is amiss, their contents could be detonated or released. From 2006 to 2015, the U.S. Department of Transportation’s Pipeline and Hazardous Materials Safety Administration (PHMSA) recorded 144,643 incidents involving a release of hazardous materials. Although there were no known instances of terrorism being the cause, accidental releases involving trucks carrying hazardous materials are not an uncommon occurrence. At this time, no systems have been developed and operationalized to monitor the movement of vehicles transporting hazardous materials. The purpose of this dissertation is to propose a comprehensive risk management system for monitoring Tier 1 Highway Security Sensitive Materials (HSSMs) which are shipped aboard commercial vehicles in the U.S. Chapter 2 examines the history and current state of hazardous materials transportation. Since the late 19th century, the federal government often introduced new regulations in response to hazardous materials incidents. However, over the past 15 years few binding policies or legislation have been enacted. This demonstrates that government agencies and the U.S. Congress are not inclined to introduce new laws and rules that could hamper business. In 2003, the Federal Motor Carrier Safety Administration (FMCSA) and other agencies led efforts to develop a prototype hazardous materials tracking system (PHTS) that mapped the location of hazardous materials shipments and quantified the level of risk associated with each one. The second half of this chapter uses an in-­‐depth gap analysis to identify deficiencies and demonstrate in what areas the prototype system does not comply with government specifications. Chapter 3 addresses the lack of customized risk equations for Tier 1 HSSMs and develops a new set of risk equations that can be used to dynamically evaluate the level of risk associated with individual hazardous materials shipments. This chapter also discusses the results of a survey that was administered to public and private industry stakeholders. Its purpose was to understand the current state of hazardous materials regulations, the likelihood of hazardous materials release scenarios, what precautionary measures can be used, and what influence social variables may have on the aggregate consequences of a hazardous materials release. The risk equation developed in this paper takes into account the survey responses as well as those risk structures already in place. The overriding goal is to preserve analytical tractability, implement a form that is usable by federal agencies, and provide stakeholders with accurate information about the risk profiles of different vehicles. Due to congressional inaction on hazardous 3 materials transportation issues, securing support from carriers and other industry stakeholders is the most viable solution to bolstering hazardous materials security. Chapter 4 presents the system architecture for The Dynamic Hazardous Materials Risk Assessment Framework (DHMRA), a GIS-­‐based environment in which hazardous materials shipments can be monitored in real time. A case study is used to demonstrate the proposed risk equation; it simulates a hazardous materials shipment traveling from Ashland, Kentucky to Philadelphia, Pennsylvania. The DHMRA maps risk data, affording security personnel and other stakeholders the opportunity to evaluate how and why risk profiles vary across time and space. DHRMA’s geo-­‐fencing capabilities also trigger automatic warnings. This framework, once fully implemented, can inform more targeted policies to enhance the security of hazardous materials. It will contribute to maintaining secure and efficient supply chains while protecting the communities that live nearest to the most heavily trafficked routes. Continuously monitoring hazardous materials provides a viable way to understand the risks presented by a shipment at a given moment and enables better, more coordinated responses in the event of a release. Implementation of DHRMA will be challenging because it requires material and procedural changes that could disrupt agency operations or business practices — at least temporarily. Nevertheless, DHRMA stands ready for implementation, and to make the shipment of hazardous materials a more secure, safe, and certain process. Although DHMRA was designed primarily with terrorism in mind, it is also useful for examining the impacts of accidental hazardous materials releases. Future iterations of DHMRA could expand on its capabilities by incorporating modeling data on the release and dispersion of toxic gases, liquids, and other substances

Simulation of a Clustering Scheme for Vehicular Ad Hoc Networks Using a DEVS-based Virtual Laboratory Environment

ANT 2018, The 9th International Conference on Ambient Systems, Networks and Technologies, Porto, PORTUGAL, 08-/05/2018 - 11/05/2018Protocol design is usually based on the functional models developed according to the needs of the system. In Intelligent Transport Systems (ITS), the features studied regarding Vehicular Ad hoc Networks (VANET) include self-organizing, routing, reliability, quality of service, and security. Simulation studies on ITS-dedicated routing protocols usually focus on their performance in specific scenarios. However, the evolution of transportation systems towards autonomous vehicles requires robust protocols with proven or at least guaranteed properties. Though formal approaches provide powerful tools for system design, they cannot be used for every types of ITS components. Our goal is to develop new tools combining formal tools such as Event-B with DEVS-based (Discrete Event System Specification) virtual laboratories in order to design the models of ITS components which simulation would allow proving and verifying their properties in large-scale scenarios. This paper presents the models of the different components of a VANET realized with the Virtual Laboratory Environment (VLE). We point out the component models fitting to formal modeling, and proceed to the validation of all designed models through a simulation scenario based on real-world road traffic data

Alaska Veterans Needs Assessment

The Institute of Social of Economic Research conducted a needs assessment of Alaska Veterans starting in the spring of 2014. Our goal was to identify and measure areas for improvement in providing services and determining the methods to achieve improvement. Our approach consisted of three methods: ‐ Survey of Alaska veterans using a list of 2,950 veterans who have requested veteran designation on their driver’s license. ‐ Focus groups: one consisting of women and one of disabled veterans. ‐ Key informant interviews with individuals responsible for helping veterans navigate the benefits available to them. Our findings are far ranging and details can be found in the report below. One of the most important lessons was the difference in needs across age groups. Younger veterans were concerned about education and employment while their older counterparts valued health care and navigating the application process. Consistent with these differences, the focus groups made it clear that targeted reminders that take into account the veteran’s life stage may be more effective. As things stand, the amount of information one is exposed to at separation can be overwhelming and intimidating. Awareness and use of federal benefits was high for health care, housing, and education benefits. Employment services were less utilized but most of our respondents were aware of their existence (Table 19). Across the board, lack of knowledge/awareness of specific benefits does not seem to be systemic. The three most claimed benefits were Health Care, Disability Compensation, Home Loans, and Education and Training. At the state level, the most commonly claimed benefits by the survey respondents are the veteran driver’s license, veterans license plates, hunting and fishing licenses, property exemption, education benefits, and veterans housing and residential loans. Of note is that only 9% claimed Veteran employment services and awareness about state benefits seems to be more of an issue than in the federal case. A third of our respondents had a disability rating of 50% or higher. Disability payments are very important across the board but seem to be essential for veterans with higher disability ratings. These payments were also more important to younger veterans who potentially have had less time to accumulate savings over their lifetime. Health care use is very much associated with age as older respondents were more likely to have applied for Health Care Services. Additionally, disability rating is also associated with frequency of health care use and utilization of VA services. Thirty percent of our respondents think they will use VA as their primary source of healthcare.Younger veterans are considerably more likely to use education benefits. The majority of our respondents used education benefits after active duty. However, more than ten percent have used education benefits both before and after and another seven percent used them only during active service. When asked about living arrangements in case a veteran could not care for themselves, it was clear that proximity to friends and family was paramount. Anchorage was chosen as the location most of them would prefer.Prepared for: Alaska Department of Military and Veterans Affairs Alaska Office of Veterans AffairsExecutive Summary / Introduction / Characteristics of Alaska Veterans and our Survey Respondents / What are the Most Critical Needs for Alaska Veterans? / What do Veterans Know about Benefits - Federal and State? / How are Veterans Using their Benefits? / What Recommendations do Veterans Have for Improving Benefits? / What are our Conclusions

Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment

Traffic incident management: A common operational picture to support situational awareness of sustainable mobility

Successful traffic incident management presupposes a multi-disciplinary approach. To meet appropriately the safety and mobility needs of all affected parties, traffic incidents call for a high level of collaboration and coordination of involved agencies. Effective traffic incident management activities rely in particular on flexible communications and information systems. Based on experiences from the military domain it is possible to develop strategic concepts that are related to the improvement of information sharing and collaboration. Such concepts can also be applied to enhanced traffic incident management information systems. The present paper aims to offer a review of the state of the art in this field and to illustrate the empirical usefulness and benefits of traffic incident management

Critical Scenario Identification for Testing of Autonomous Driving Systems

Background: Autonomous systems have received considerable attention from academia and are adopted by various industrial domains, such as automotive, avionics, etc. As many of them are considered safety-critical, testing is indispensable to verify their reliability and safety. However, there is no common standard for testing autonomous systems efficiently and effectively. Thus new approaches for testing such systems must be developed.Aim: The objective of this thesis is two-fold. First, we want to present an overview of software testing of autonomous systems, i.e., relevant concepts, challenges, and techniques available in academic research and industry practice. Second, we aim to establish a new approach for testing autonomous driving systems and demonstrate its effectiveness by using real autonomous driving systems from industry.Research Methodology: We conducted the research in three steps using the design science paradigm. First, we explored the existing literature and industry practices to understand the state of the art for testing of autonomous systems. Second, we focused on a particular sub-domain - autonomous driving - and proposed a systematic approach for critical test scenario identification. Lastly, we validated our approach and employed it for testing real autonomous driving systems by collaborating with Volvo Cars.Results: We present the results as four papers in this thesis. First, we conceptualized a definition of autonomous systems and classified challenges and approaches, techniques, and practices for testing autonomous systems in general. Second, we designed a systematic approach for critical test scenario identification. We employed the approach for testing two real autonomous driving systems from the industry and have effectively identified critical test scenarios. Lastly, we established a model for predicting the distribution of vehicle-pedestrian interactions for realistic test scenario generation for autonomous driving systems. Conclusion: Critical scenario identification is a favorable approach to generate test scenarios and facilitate the testing of autonomous driving systems in an efficient way. Future improvement of the approach includes (1) evaluating the effectiveness of the generated critical scenarios for testing; (2) extending the sub-components in this approach; (3) combining different testing approaches, and (4) exploring the application of the approach to test different autonomous systems

Automated Scenario Generation Using Halton Sequences for the Verification of Autonomous Vehicle Behavior in Simulation

As autonomous vehicles continue to develop, verifying their safety remains a large hurdle to mass adoption. One component of this is testing, however it has been shown that it is impractical to statistically prove an autonomous vehicle’s safety using real-world testing alone. Therefore, simulation tools and other virtual testing methods are being employed to assist with the verification process. Testing in simulation still faces some of the challenges of the real world, such as the difficulty in exhaustively testing the system in all scenarios it will encounter. Manual scenario creation is time consuming and does not guarantee scenario coverage. Pseudo-random scenario generation is a faster option, but still does not ensure coverage of the state space. Therefore, this study proposes the use of Halton sequences to automatically generate scenarios for autonomous vehicle testing in simulation. It compares these scenarios against a set of pseudo-randomly generated scenarios and assesses the performance of each method to cover the simulation state space and provide an accurate depiction of the capabilities of the system-under-test. These tests are carried out in the CARLA simulation environment on an open source, published driving model called “Learning by Cheating” which takes place as the system-under-test. This study concludes that the scenario set generated by the Halton sequence is better at providing an accurate representation of the capabilities of the system-under-test than the pseudo-random scenario generation method

Component and system design of a mild hybrid 48 V powertrain for a light vehicle

This thesis presents contributions in three areas relevant for the development of 48 V mild hybrid electric powertrains for cars. The first part comprises methodologies and extensive testing of lithium-ion battery cells in order to establish the electric and thermal performance using equivalent circuit models.\ua0 Empirical, lumped-parameter models are used to ensure fast simulation execution using only linear circuit elements. Both electrochemical impedance spectroscopy and high-current pulse discharge testing is used to extract model parameters. Plenty of parameter results are published for various cells, temperatures and SOC levels. Further on, the model accuracy in voltage response is also evaluated. It is found that an R+2RC equivalent circuit offers the lowest error, 11 mV RMSE in a 1.5 h drive cycle, which is among the lowest numbers found in the literature for similar models. In the second part, electric machines with tooth-coil windings are explored as a viable candidate for mild hybrids. First, a method of analytically calculating the high-level electro-magnetic properties for all possible combinations of three-phase, dual layer tooth-coil winding machines is established and presented in a graphically appealing manner.\ua0 Then, a pair of pseudo-6-phase 50 kW PMSMs are designed, constructed and validated in a custom designed calorimetric dynamo test stand. These machines feature in-stator and in-slot forced oil cooling, enabling very high current densities of 25\ua0A/mm\ub2 continuous and 35\ua0A/mm\ub2 peak. A high net power density (19 kW/l) and a large area of high peak efficiency (95%) is shown numerically and validated by calorimetric measurements. Finally, low-level design, construction and evaluation of 48 V inverter hardware is explored. By using high-performance, extra-low-voltage silicon-based MOSFETs with custom designed metal substrate printed circuit boards, custom made gate drivers, and water cooling, 3x220 A RMS is reached experimentally on a 154 cm\ub2 area and an efficiency of 95.6%