Road Vehicle Automation 8

SIP, or Cross-ministerial Strategic Innovation Promotion Program is a 5 year R&D program led by the Japan government. SIP-adus, or automated driving system for universal service is a project for the realization for automated driving systems (ADS) with Government-Industry-Academia cooperation. We tackle various issues such as R&D, regulation reform, public acceptance creation, international cooperation for realization of cooperative ADS together. The project focused on cooperative R&D themes such as Dynamic Map, Safety assurance, Cybersecurity, and so on. The status of several examples of our challenge will be reported.

This chapter outlines the key developments and issues in the U.S. national, state and local automated vehicle (AV) policy landscape. There are a range of perspectives among U.S. government and private-sector stakeholders. Despite the wide range of viewpoints, the authors aim to underscore where there is agreement on key policy issues, and where there are opportunities for more discussion. Federal policy to date has focused on a voluntary system for ensuring safety of AVs. Federal regulatory actions have been limited to guidance documents, although the government recently proposed a safety framework for the automated driving system (ADS). States have varied in their approaches to AV policy and this chapter summarizes research coding state activities on a spectrum from more restrictive, such as California, to more permissive, such as Arizona. In the final section of the chapter the authors discuss the role of interstate coalitions. This Chapter is organized into five sections:

Automated vehicles come with a range of safety risks, that could result in deaths or serious injuries on the road. Companies will seek to address these risks in the initial design of the automated driving system. However, automated vehicles will need to operate safely not just on day one, but throughout their lifetime on the road. On-road or “in-service” safety risks could result from degradation of the vehicle or changes to the outside environment. Companies developing automated driving systems will need to manage these ongoing safety risks; government will need to ensure appropriate regulation of these risks.

This chapter focuses on local policy roadmaps. While a significant body of policy has been developed at the national and state levels, very little policy work has identified policy for local government. In this context, this chapter reviews state of technology and policy development and offers potential policy concepts for local government. Suggestions from a workshop are offered and then dialogued in a structured format. This yields ideas for high-impact, low-cost policy solutions that can help cities better prepare for automation.

This chapter summarizes the presentations of speakers addressing such issues during the Automated Vehicles Symposium 2020 (AVS20) held virtually on July 27–30, 2020. These speakers participated in the break-out session titled “Artificial Intelligence for Automated Vehicle Control and Traffic Operations: Challenges and Opportunities”. The corresponding discussion and recommendations are presented in terms of the lessons learned and the future research directions to be adopted to benefit from AI in order to develop safer and more efficient connected and automated vehicles (CAV). This session was organized by the Transportation Research Board (TRB) Committee on Traffic Flow Theory and Characteristics (ACP50) and the TRB Committee on Artificial Intelligence and Advanced Computing Applications (AED50).

This chapter presents information on automated shuttles and buses, which are being piloted, demonstrated, and deployed in downtown areas, university campuses, business parks, entertainment complexes, and other areas. The chapter focuses on the presentations and discussions at a breakout session at the 2020 Automated Vehicle Symposium (AVS). The session and this chapter highlight the experience planning, procuring, operating, and evaluating automated shuttles and buses to help inform future decision-making. Many projects were on hold in 2020 as a result of the pandemic, or pivoted to food delivery or other alternate uses. Areas for additional research and ongoing information sharing are also summarized.

Automated Vehicles rely on intelligent systems to enable safe and efficient transportation. Thanks to robust perception and reliable communication, automated vehicles will reshape transportation services. However, the security of automated vehicles has to be guaranteed at the component level. In this chapter, we provide an overview of two threats to connected and automated vehicles (CAV), namely adversarial AI in perception system, and impact of Quantum Computer on CAV security.

Cooperative Adaptive Cruise Control (CACC) systems uses wireless connectivity to guarantee string stability for platooning, which Adaptive Cruise Control (ACC) fails to provide. One of the constraints that hinders widespread adoption of CACC is that almost all developments and real validations have been done on strings of identical vehicles. This chapter summarizes the most recent efforts in the development of a generic control architecture that enables CACC on strings of vehicles of different makes/models/types, dynamics and powertrains. The developed hierarchical approach has demonstrated feasibility of CACC system on real vehicles, even at short time gaps. It is also robust in handling cut-in and cut-out maneuvers of other vehicles in public traffic.

This Chapter is intended to lay the background for meaningful conversations between public sector and private industry, to understand the barriers and needs for automated driving systems (ADS) and AV testing and evaluation. This includes understanding the capabilities that need to be implemented both in the ADS and on the roadway to support safe and effective ADS operation and understanding what is needed to design and conduct joint tests or pilots. This session aims to identify a path forward to overcome specific, private and public sector barriers, facilitate, and advance AV testing and deployment.