Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.
Mobility is undergoing a “horses to cars”-sized shift that will reverberate across business and society for generations. Future of Mobility is mainly driven by 4 main pillars viz. Connected, Electrified, Automated and Shared Driving. With advancement in Communication Technology supplemented by huge customer base, Connectivity has proven to deliver better Services to the End-user. Connected Mobility is going to be the next Big Thing in the Mobility Arena. In this paper, we will try to qualitatively explore what Connected Mobility is all about and what it has to offer in terms of - Opportunities on one side as well as new challenges that were never witnessed in the realm of Mobility in the Past, with focus on the 2 wheeler segment. This paper focuses on Opportunities in terms of Location Based services, Vehicle Management, Data Analytics, Infotainment and possible Business scenarios and Models as well as challenges in Terms of Security and Data Ownership
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the new generation of sensors designed for vehicles capable of automated driving. Four main issues are outlined that merit immediate interest: First, specifying a standardized terminology and taxonomy to be used for discussing the sensors required by automated vehicles. Second, generating standardized tests and procedures for verifying, simulating, and calibrating automated driving sensors. Third, creating a standardized set of tools and methods to ensure the security, robustness, and integrity of data collected by such sensors. The fourth issue, regarding the ownership and privacy of data collected by automated vehicle sensors, is considered only briefly here since its scope far exceeds the technical issues that are the primary focus of the present report. SAE EDGE™ Research Reports are preliminary investigations of new technologies.
This paper and the associated lecture present an overview of technology trends and of market and business opportunities created by technology, as well as of the challenges posed by environmental and economic considerations. Commercial vehicles are one of the engines of our economy. Moving goods and people efficiently and economically is a key to continued industrial development and to strong employment. Trucks are responsible for nearly 70% of the movement of goods in the USA (by value) and represent approximately 300 billion of the 3.21 trillion annual vehicle miles travelled by all vehicles in the USA while public transit enables mobility and access to jobs for millions of people, with over 10 billion trips annually in the USA creating and sustaining employment opportunities.
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.
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.
Transportation departments are under-going a dramatic transformation, shifting from organizations focused primarily on building roads to a focus on mobility for all users. The transformation is the result of rapidly advancing autonomous vehicle technology and personal telecommunication technology. These technologies provide the opportunity to dramatically improve safety, mobility, and economic opportunity for society and industry. Future generations of engineers and other transportation professionals have the opportunity to be part of that societal change. This paper will focus on the technologies state DOT’s and the private sector are researching, developing, and deploying to promote the future of mobility and improved efficiency for commercial trucking through advancements in truck platooning, self-driving long-haul trucking, and automated last mile distribution networks.
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the challenges of determining the optimal balance for testing automated driving systems (ADS). Three main issues are outlined that merit immediate interest: First, determining what kind of testing an ADS needs before it is ready to go on the road. Second, the current, optimal, and realistic balance of simulation testing and real-world testing. Third, the challenges of sharing data in the industry. SAE EDGE™ Research Reports are preliminary investigations of new technologies. The three technical issues identified in this report should be discussed in greater depth with the aims of, first, clarifying the scope of the industry-wide alignment needed; second, prioritizing the issues requiring resolution; and, third, creating a plan to generate the necessary frameworks, practices, and protocols.
Autonomous driving systems and connected mobility are the next big developments for the car manufacturers and their suppliers during the next decade. To achieve the high computing power needs and fulfill new upcoming requirements due to functional safety and security, heterogeneous processor architectures with a mixture of different core architectures and hardware accelerators are necessary. To tackle this new type of hardware complexity and nevertheless stay within monetary constraints, high performance computers, inspired by state of the art data center hardware, could be adapted in order to fulfill automotive quality requirements. The European Processor Initiative (EPI) research project tries to come along with that challenge for next generation semiconductors. To be as close as possible to series development needs for the next upcoming car generations, we present a hybrid semiconductor system-on-chip architecture for automotive.
Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems.
This SAE EDGE™ Research Report identifies key unsettled issues of interest to the automotive industry regarding the challenges of achieving optimal model fidelity for developing, validating, and verifying vehicles capable of automated driving. Three main issues are outlined that merit immediate interest: First, assuring that simulation models represent their real-world counterparts, how to quantify simulation model fidelity, and how to assess system risk. Second, developing a universal simulation model interface and language for verifying, simulating, and calibrating automated driving sensors. Third, characterizing and determining the different requirements for sensor, vehicle, environment, and human driver models. SAE EDGE™ Research Reports are preliminary investigations of new technologies.
Technological advances in both hardware (Nano-electronics) and software (artificial intelligence) are increasingly influencing our lives on equipment and devices that surrounds us and more recently our means of locomotion. The autonomous vehicles, which previously appeared only in movie scenes, can already be found in our environment, such as ships, cars, trucks, tractors and aero engines. Considering the autonomous vehicles, its launching is much closer than we could imagine, since many companies signalize having the conditions to launch them in a large scale within 2018 year. The insertion of this type of technology opens a range of advances related to vehicles and the environment in which it is inserted. The communication between the vehicles, roads and people can be highlighted. These advances reveal a series of benefits to the customer such as free time during the route, higher safety, etc.
This SAE EDGE Research Report addresses the unsettled topic of user acceptance of automated driving, analyzing the user experience for a more intuitive and safe driving experience. Unsettled Topics Concerning User Experience and Acceptance of Automated Vehicles examines the requirements for safer driver/user engagement with driving for the various SAE automation levels. It analyzes consumer sentiment toward automated driving - both consumer excitement about the perceived benefits and dislikes or concerns about the technology. The findings from surveys about drivers' experience with advanced driving assistance technologies and its application to automated driving is also brought to the surface of the discussion, together with driver profiles observed during a user-centric experience in an immersive automated driving cockpit.
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.
Automated driving systems (ADS) represent an area of considerable investment and activity within the transportation sphere. The potential impact of ADS on safety, efficiency, and user experience are extremely significant. To get the most from the technology, it is important to ensure that policies are developed to support the balance between achieving public sector objectives and supporting private sector innovation. This SAE EDGE™ Research Report explores the policy aspects related to ADS technology, explains the key stakeholders, identifies unsettled issues, and proposes a number of steps to move forward and improve the current situation. It is hoped that the report will provide a valuable resource to those involved in the definition of ADS policy from both public and private perspectives. It is also intended to serve as a resource for those involved in ADS planning and development and public sector staff involved in other aspects beyond ADS policy.