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Author: Event Data Recorder Committee Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This SAE Recommended Practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing the performance of automated driving system (ADS) during an event that meets the trigger threshold criteria specified in this document. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify a common ADS data logger record format as applicable for motor vehicle applications.Automated driving systems (ADSs) perform the complete dynamic driving task (DDT) while engaged. In the absence of a human "driver," the ADS itself could be the only witness of a collision event. As such, a definition of the ADS data recording is necessary in order to standardize information available to the accident reconstructionist. For this purpose, the data elements defined herein supplement the SAE J1698-1 defined EDR in order to facilitate the determination of the background and events leading up to a collision in an ADS-operated vehicle.The data elements defined in this document are unique to Level 3, 4, or 5 ADS features, as defined by SAE J3016, and provide additional background of the events leading up to a crash or crash-like event. The data from sensors such as camera(s), LiDAR(s) etc. will provide information in the absence of a human driver. The data included in the ADS data logger is expected to be used in conjunction with the SAE J1698 event data recorder (EDR) record and traditional accident reconstruction analysis. The EDR and ADS data logger will capture information leading up to the triggered event, at a minimum. There are no facts to support that recording data for greater than 5 seconds pre-event would change the outcome of any crash analysis. Thus, the recommended recording duration for a data logger is 5 seconds pre-event, same as an EDR. Due to the potential for sensor and/or communication failure during a crash event, the recommendation is that data should be collected post-crash for impact and rollover sensors for up to 250 ms.ADS technology is still being developed and is not yet commercially deployed. Therefore, this SAE Recommended Practice is intended as a guide toward standard practice and is subject to change to keep pace with experience and technical advances. This document is being revised in order to harmonize, where applicable, with information from the Automated Vehicle Safety Consortium (AVSC) Best Practice for Data Collection for Automated Driving System - Dedicated Vehicles (ADS-DVs) to Support Event Analysis.
Author: Event Data Recorder Committee Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This SAE Recommended Practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing the performance of automated driving system (ADS) during an event that meets the trigger threshold criteria specified in this document. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify a common ADS data logger record format as applicable for motor vehicle applications.Automated driving systems (ADSs) perform the complete dynamic driving task (DDT) while engaged. In the absence of a human "driver," the ADS itself could be the only witness of a collision event. As such, a definition of the ADS data recording is necessary in order to standardize information available to the accident reconstructionist. For this purpose, the data elements defined herein supplement the SAE J1698-1 defined EDR in order to facilitate the determination of the background and events leading up to a collision in an ADS-operated vehicle.The data elements defined in this document are unique to Level 3, 4, or 5 ADS features, as defined by SAE J3016, and provide additional background of the events leading up to a crash or crash-like event. The data from sensors such as camera(s), LiDAR(s) etc. will provide information in the absence of a human driver. The data included in the ADS data logger is expected to be used in conjunction with the SAE J1698 event data recorder (EDR) record and traditional accident reconstruction analysis. The EDR and ADS data logger will capture information leading up to the triggered event, at a minimum. There are no facts to support that recording data for greater than 5 seconds pre-event would change the outcome of any crash analysis. Thus, the recommended recording duration for a data logger is 5 seconds pre-event, same as an EDR. Due to the potential for sensor and/or communication failure during a crash event, the recommendation is that data should be collected post-crash for impact and rollover sensors for up to 250 ms.ADS technology is still being developed and is not yet commercially deployed. Therefore, this SAE Recommended Practice is intended as a guide toward standard practice and is subject to change to keep pace with experience and technical advances. This document is being revised in order to harmonize, where applicable, with information from the Automated Vehicle Safety Consortium (AVSC) Best Practice for Data Collection for Automated Driving System - Dedicated Vehicles (ADS-DVs) to Support Event Analysis.
Author: Plato Pathrose Publisher: SAE International ISBN: 1468604139 Category : Transportation Languages : en Pages : 279
Book Description
The day will soon come when you will be able to verbally communicate with a vehicle and instruct it to drive to a location. The car will navigate through street traffic and take you to your destination without additional instruction or effort on your part. Today, this scenario is still in the future, but the automotive industry is racing to toward the finish line to have automated driving vehicles deployed on our roads. ADAS and Automated Driving: A Practical Approach to Verification and Validation focuses on how automated driving systems (ADS) can be developed from concept to a product on the market for widescale public use. It covers practically viable approaches, methods, and techniques with examples from multiple production programs across different organizations. The author provides an overview of the various Advanced Driver Assistance Systems (ADAS) and ADS currently being developed and installed in vehicles. The technology needed for large-scale production and public use of fully autonomous vehicles is still under development, and the creation of such technology is a highly innovative area of the automotive industry. This text is a comprehensive reference for anyone interested in a career focused on the verification and validation of ADAS and ADS. The examples included in the volume provide the reader foundational knowledge and follow best and proven practices from the industry. Using the information in ADAS and Automated Driving, you can kick start your career in the field of ADAS and ADS.
Author: Elgharbawy, Mohamed Publisher: KIT Scientific Publishing ISBN: 3731512548 Category : Technology & Engineering Languages : en Pages : 268
Book Description
With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria.
Author: Laura Fraade-Blanar Publisher: ISBN: 9781977401649 Category : Technology & Engineering Languages : en Pages : 0
Book Description
This report presents a framework for measuring safety in automated vehicles (AVs): how to define safety for AVs, how to measure safety for AVs, and how to communicate what is learned or understood about AVs.
Author: Event Data Recorder Committee Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
This SAE Recommended Practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing vehicle crash and crash-like events that meet specified trigger criteria. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify EDR record format as applicable for light-duty motor vehicle Original Equipment applications. This SAE Recommended Practice is being revised to remove references to automated driving system (ADS) Level 3, 4, and 5 and SAE J3016, which will now be covered in SAE J3197, and to make updates based on recent discussions in the SAE EDR Committee.
Author: Weisong Shi Publisher: Springer Nature ISBN: 3030815641 Category : Computers Languages : en Pages : 239
Book Description
This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.This book on computing systems for autonomous driving takes a comprehensive look at the state-of-the-art computing technologies, including computing frameworks, algorithm deployment optimizations, systems runtime optimizations, dataset and benchmarking, simulators, hardware platforms, and smart infrastructures. The objectives of level 4 and level 5 autonomous driving require colossal improvement in the computing for this cyber-physical system. Beginning with a definition of computing systems for autonomous driving, this book introduces promising research topics and serves as a useful starting point for those interested in starting in the field. In addition to the current landscape, the authors examine the remaining open challenges to achieve L4/L5 autonomous driving. Computing Systems for Autonomous Driving provides a good introduction for researchers and prospective practitioners in the field. The book can also serve as a useful reference for university courses on autonomous vehicle technologies.
Author: James M. Anderson Publisher: Rand Corporation ISBN: 0833084372 Category : Transportation Languages : en Pages : 215
Book Description
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.
Author: Event Data Recorder Committee
Author: Event Data Recorder Committee
Author: Plato Pathrose
Author: Elgharbawy, Mohamed
Author: Gereon Meyer
Author: Laura Fraade-Blanar
Author: Event Data Recorder Committee
Author: Weisong Shi
Author: 
Author: 
Author: James M. Anderson