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The impending deployment of automated vehicles (AVs) represents a major shift in the traditional approach to ground transportation; its effects will inevitably be felt by parties directly involved with the vehicle manufacturing and use (e.g., automotive original equipment manufacturers (OEMs), public transportation systems, heavy goods transportation providers) and those that play roles in the mobility ecosystem (e.g., aftermarket and maintenance industries, infrastructure and planning organizations, automotive insurance providers, marketers, telecommunication companies). The focus of this SAE EDGE Research Report is to address a topic overlooked by many who choose to view automated driving systems and AVs from a "10,000-foot perspective: " the topic of how AVs will communicate with other road users such as conventional (human-driven) vehicles, bicyclists, and pedestrians while in operation. This unsettled issue requires assessing the spectrum of existing modes of communication - both implicit and explicit, both biological and technological - employed by road users today. NOTE: SAE EDGE(TM) 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(TM) Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE(TM) Research Reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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 report explores the policy aspects related to ADS, 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.
Unsettled Topics in Automated Vehicle Data Sharing for Verification and Validation Purposesdiscusses 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.
This document provides preliminary1 safety-relevant guidance for in-vehicle fallback test driver training and for on-road testing of vehicles being operated by prototype conditional, high, and full (Levels 3 to 5) ADS, as defined by SAE J3016. It does not include guidance for evaluating the performance of post-production ADS-equipped vehicles. Moreover, this guidance only addresses testing of ADS-operated vehicles as overseen by in-vehicle fallback test drivers (IFTD).These guidelines do not address: Remote driving, including remote fallback test driving of prototype ADS-operated test vehicles in driverless operation. (Note: The term "remote fallback test driver" is included as a defined term herein and is intended to be addressed in a future iteration of this document. However, at this time, too little is published or known about this type of testing to provide even preliminary guidance.) Testing of driver support features (i.e., Levels 1 and 2), which rely on a human driver to perform part of the dynamic driving task (DDT) and to supervise the driving automation feature's performance in real time. (Refer to SAE J3016.) Closed-course testing. Simulation testing (except for training purposes). Component-level testing.These guidelines also do not address prototype vehicle and IFTD performance data collection and retention. The collection of data invokes various legal and risk management considerations that users of this document should nevertheless bear in mind, such as: Maintaining auditable procedures and documentation. Adhering to applicable privacy laws and principles. Ensuring adequate data collection and recording integrity to support post-crash forensic analysis. This document provides safety-relevant guidance for in-vehicle fallback test driver training and for testing prototype automated driving systems (ADS) equipped on test vehicles operated in mixed-traffic environments on public roads (hereafter, prototype ADS-operated vehicles). This document is being substantially updated in order to incorporate content from Automated Vehicle Safety Consortium (AVSC) publication 00001201911: "AVSC Best Practice for In-Vehicle Fallback Test Driver Selection, Training, and Oversight Procedures for Automated Vehicles Under Test" and to re-classify this document as an SAE Recommended Practice, rather than an SAE Information Report.It is assumed that the prototype ADS-operated vehicles that are the subject of this guidance have been developed using standardized methods for safer product development including, but not limited to: A systems engineering approach (i.e., V-model). Adherence to a recognized system safety process(es) for identifying hazards and implementing strategies for mitigating them. Implementation of an electrical/electronic (E/E) architecture (system/hardware/software levels) capable of implementing hazard mitigation concepts and strategies. Analysis and testing of identified hazard mitigation strategies (hardware and software).Prototype ADS-operated vehicles that are based on existing production vehicles rely on the existing vehicle's E/E architecture, as adapted for ADS. Prototype ADS technology provided via added hardware and software modules that are not integrated according to the vehicle manufacturer's specifications, should be checked to ensure that they do not interfere with base vehicle hardware or software systems. As such, they should abide by the following general principles: All hardware and software interfaces between production- and development-level hardware and software should be analyzed and tested for operational integrity, including analysis of failure modes and effects. Developmental software added to a vehicle (including that equipped on added hardware modules) should be monitored and/or include self-diagnostics for safety-critical functions, which should be verified for efficacy prior to on-road testing. Alternatively, system-level approaches to ensuring developmental software safety (e.g., shadow mode testing) is also acceptable.Test program/operations management plays a key role in helping to maintain safety while conducting on-road testing of prototype ADS-operated vehicles. Unexpected behaviors (including incidents) should be reported accurately and consistently for later root-cause analysis and resolution. A manager in charge of prototype ADS-operated vehicle testers should explain to them the organization's specific rules about testing and documentation, as well as any hardware/software updates that impact the performance of the ADS-operated vehicles. Novice testers should be paired with more experienced testers to learn the appropriate reactions in various situations.Real-time calibration/tuning of ADS software during testing should be allowed only after evaluation by qualified personnel (e.g., development engineer, lead calibrator, and/or designated safety engineer), indicating that the change does not pose unacceptable risk for on-road testing.
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(TM) 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(TM) Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE(TM) Research Reports are not intended to resolve the issues they identify or close any topic to further scrutiny.
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
Current advanced driver-assistance systems (ADAS) and automated driving systems (ADS) rely on high-definition (HD) maps to enable a range of features and functions. These maps can be viewed as an additional sensor from an ADAS or ADS perspective as they impact overall system confidence, reduce system computational resource needs, help improve comfort and convenience, and ultimately contribute to system safety. However, HD mapping technology presents multiple challenges to the automotive industry. Unsettled Issues on HD Mapping Technology for Autonomous Driving and ADAS identifies the current unsettled issues that need to be addressed to reach the full potential of HD maps for ADAS and ADS technology and suggests some possible solutions for initial map creation, map change detection and updates, and map safety levels.