Automated driving system (ADS) technology and ADS-enabled/operated vehicles - commonly referred to as automated vehicles and autonomous vehicles (AVs) - have the potential to impact the world as significantly as the internal combustion engine. Successful ADS technologies could fundamentally transform the automotive industry, civil planning, the energy sector, and more. Rapid progress is being made in artificial intelligence (AI), which sits at the core of and forms the basis of ADS platforms. Consequently, autonomous capabilities such as those afforded by advanced driver assistance systems (ADAS) and other automation solutions are increasingly becoming available in the marketplace. To achieve highly or fully automated or autonomous capabilities, a major leap forward in the validation of these ADS technologies is required. Without this critical cog, helping to ensure the safety and reliability of these systems and platforms, the full capabilities of ADS technology will not be realized. This paper explores the ADS validation challenge by reviewing existing approaches and examining the effectiveness of those approaches, presenting critical techniques required to bring safe and effective solutions to market, discussing unsettled topics, and suggesting next steps for industry stakeholders to consider as they work to advance the ADS ecosystem. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2019001

The Federal Aviation Administration (FAA) and the Department of Transportation’s (DOT’s) National Highway Traffic Safety Administration (NHTSA) face similar challenges regarding the regulation of autonomous systems powered by artificial intelligence (AI) algorithms that replace the human factor in the decision-making process. Validation and verification (V&V) processes contribute to implementation of correct system requirements and the development life cycle - starting with the definition of regulatory, marketing, operational, performance, and safety requirements. The V&V process is one of the steps of a development life cycle starting with the definition of regulatory, marketing, operational, performance, and safety requirements. They define what a product is, and they flow down into lower level requirements defining control architectures, hardware, and software. The industry is attempting to define regulatory requirements and a framework to gain safety clearance of such products. This report suggests a regulatory text and a safety and V&V approach from an aerospace engineering perspective assessing the replacement of the human driver from the decision-making role by a computational system. It also suggests an approach where aerospace guidelines can be used alongside the International Organization for Standardization (ISO) standard ISO 26262 in order to define a viable and valuable framework for autonomous systems safety clearance (or certification). NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2019012

With over 100 years of operation, the current automobile industry has settled into an equilibrium with the development of methodologies, regulations, and processes for improving safety. In addition, a nearly $2-trillion market operates in the automotive ecosystem with connections into fields ranging from insurance to advertising. Enabling this ecosystem is a well-honed, tiered supply chain and an established development environment. Autonomous vehicle (AV) technology is a leap forward for the existing automotive industry; now the automobile is expected to manage perception and decision-making tasks. The safety technologies associated with these tasks were presented in an earlier SAE EDGE™ Research Report, “Unsettled Technology Areas in Autonomous Vehicle Test and Validation.” In a later SAE EDGE™ Research Report, “Unsettled Topics Concerning Automated Driving Systems and the Transportation Ecosystem,” senior executives from the automotive ecosystem explored the impact of AV technology as they faced the prospect of this disruptive technology entering their marketplace. Interestingly, stable use-models and market penetration were all gated primarily by the demonstration of AV safety. Building on these previous verification and validation (V&V)-related reports, “Unsettled Topics Concerning Automated Driving Systems and the Development Ecosystem” explores the open issues in the shift of the development and supplier environment toward a new AV-enabled future. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the challenges they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2020004

Over the last 100 years, the automobile has become integrated in a fundamental way into the broader economy. A broad and deep ecosystem has emerged, and critical components of this ecosystem include insurance, after-market services, automobile retail sales, automobile lending, energy suppliers (e.g., gas stations), medical services, advertising, lawyers, banking, public planners, and law enforcement. These components - which together represent almost $2 trillion of the U.S. economy - are in equilibrium based on the current capabilities of automotive technology. However, the advent of autonomous vehicles (AVs) and technologies like electrification have the potential to significantly disrupt the automotive ecosystem. The critical cog governing the rate and pace of this shift is the management of the test and verification of AVs. In this SAE EDGE™ report, six senior industry leaders in the impacted ecosystems essay articles which describe sectors of the current automotive ecosystem and the manner in which AV technology can potentially reshape them - providing a mosaic of the massive infrastructure shifts which will be required to absorb AV technologies. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2019005

This book gathers papers from the 23rd International Forum on Advanced Microsystems for Automotive Applications (AMAA 2020) held online from Berlin, Germany, on May 26-27, 2020. Focusing on intelligent system solutions for auto mobility and beyond, it discusses in detail innovations and technologies enabling electrification, automation and diversification, as well as strategies for a better integration of vehicles into the networks of traffic, data and power. Further, the book addresses other relevant topics, including the role of human factors and safety issues in automated driving, solutions for shared mobility, as well as automated bus transport in rural areas. Implications of current circumstances, such as those generated by climate change, on the future development of auto mobility, are also analysed, providing researchers, practitioners and policy makers with an authoritative snapshot of the state-of-the-art, and a source of inspiration for future developments and collaborations.

Modern automobiles collect around 25 gigabytes of data per hour and autonomous vehicles are expected to generate more than 100 times that number. In comparison, the Apollo Guidance Computer assisting in the moon launches had only a 32-kilobtye hard disk. Without question, the breadth of in-vehicle data has opened new possibilities and challenges. The potential for accessing this data has led many entrepreneurs to claim that data is more valuable than even the vehicle itself. These intrepid data-miners seek to explore business opportunities in predictive maintenance, pay-as-you-drive features, and infrastructure services. Yet, the use of data comes with inherent challenges: accessibility, ownership, security, and privacy. Unsettled Legal Issues Facing Data in Autonomous, Connected, Electric, and Shared Vehicles examines some of the pressing questions on the minds of both industry and consumers. Who owns the data and how can it be used? What are the regulatory regimes that impact vehicular data use? Is the US close to harmonizing with other nations in the automotive data privacy? And will the risks of hackers lead to the “zombie car apocalypse” or to another avenue for ransomware? This report explores a number of these legal challenges and the unsettled aspects that arise in the world of automotive data. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2021019

Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposes discusses the unsettled issue of sharing the terabytes of driving data generated by Automated Vehicles (AVs) on a daily basis. Perception engineers use these large datasets to analyze and model the automated driving systems (ADS) that will eventually be integrated into future “self-driving” vehicles. However, the current industry practices of collecting data by driving on public roads to understand real-world scenarios is not practical and will be unlikely to lead to safe deployment of this technology anytime soon. Estimates show that it could take 400 years for a fleet of 100 AVs to drive enough miles to prove that they are as safe as human drivers. Yet, data-sharing can be developed – as a technology, culture, and business – and allow for rapid generation and testing of the billions of possible scenarios that are needed to prove practicality and safety of an ADS – resulting in lower research and development costs to the industry. Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposes explores how this could lead to better regulation, insurance, public acceptance – and finally, shorter technology development cycles. Finding a business case and changing to an open data culture are not going to be easy tasks, but data sharing is the only way forward for the whole industry to move to the next phase of deployment after nearly a decade of intense research. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2020007

As automobiles morph from stand-alone mechanical objects to highly connected, autonomous systems with increasing amounts of electronic components. To manage these complex systems, some semblance of in-car decision-making is also being built and networked to a cloud architecture. This cloud can also enable even deeper capabilities within the broader automotive ecosystem. Unsettled Issues Regarding Autonomous Vehicles and Open-source Software introduces the impact of software in advanced automotive applications, the role of open-source communities in accelerating innovation, and the important topic of safety and cybersecurity. As electronic functionality is captured in software and a bigger percentage of that software is open-source code, some critical challenges arise concerning security and validation. https://doi.org/10.4271/EPR2021009

The two-volume set LNAI 12033 and 11034 constitutes the refereed proceedings of the 12th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2020, held in Phuket, Thailand, in March 2020. The total of 105 full papers accepted for publication in these proceedings were carefully reviewed and selected from 285 submissions. The papers of the first volume are organized in the following topical sections: Knowledge Engineering and Semantic Web, Natural Language Processing, Decision Support and Control Systems, Computer Vision Techniques, Machine Learning and Data Mining, Deep Learning Models, Advanced Data Mining Techniques and Applications, Multiple Model Approach to Machine Learning. The papers of the second volume are divided into these topical sections: Application of Intelligent Methods to Constrained Problems, Automated Reasoning with Applications in Intelligent Systems, Current Trends in Arti cial Intelligence, Optimization, Learning,and Decision-Making in Bioinformatics and Bioengineering, Computer Vision and Intelligent Systems, Data Modelling and Processing for Industry 4.0, Intelligent Applications of Internet of Things and Data AnalysisTechnologies, Intelligent and Contextual Systems, Intelligent Systems and Algorithms in Information Sciences, Intelligent Supply Chains and e-Commerce, Privacy, Security and Trust in Arti cial Intelligence, Interactive Analysis of Image, Video and Motion Data in LifeSciences.

The current business model of the automotive industry is based on individual car ownership, yet new ridesharing companies such as Uber and Lyft are well capitalized to invest in large, commercially operated, on-demand mobility service vehicle fleets. Car manufacturers like Tesla want to incorporate personal car owners into part-time fleet operation by utilizing the company’s fleet service. These robotaxi fleets can be operated profitably when the technology works in a reliable manner and regulators allow driverless operation. Although Mobility-as-a-Service (MaaS) models of private and commercial vehicle fleets can complement public transportation models, they may contribute to lower public transportation ridership and thus higher subsidies per ride. This can lead to inefficiencies in the utilization of existing public transportation infrastructure. MaaS platforms can also cause a reduced reliance on parking infrastructure (e.g., street parking lanes and parking garages) which can contribute to an improvement in overall traffic flow, and a reduction in capital investment for commercial and residential real-estate development. Urban planning can be better centered around the true mobility needs of the citizens without sacrificing valuable space for vehicles that are inactive most of the time. A key challenge is the transition phase where traditional forms of car ownership and transportation coexist with new forms of mobility services (before true MaaS platforms are fully optimized). Another fundamental issue is determining the true cost of car ownership and establishing ideal cost structures for on-demand mobility services in order to replace the need of owning a car. This needs to occur without compromising subsidized public transportation which provides affordable transportation for low-income groups. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2019004