Browse Topic: Cooperative driving automation

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Vehicle Experience and Human Interaction: Vol. 1EPRCOMPV17202305/03/2024
Connected and autonomous vehicles (CAVs) and their productization are a major focus of the automotive and mobility industries as a whole. However, despite significant investments in this technology, CAVs are still at risk of collisions, particularly in unforeseen circumstances or “edge cases.” It is also critical to ensure that redundant environmental data are available to provide additional information for the autonomous driving software stack in case of emergencies. Additionally, vehicle-to-everything (V2X) technologies can be included in discussions on safer autonomous driving design. Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including CAVs. It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The
Abdul Hamid, Umar Zakir, Roth, Christian, Nickerson, Jeffrey, Lyytinen, Kalle, King, John Leslie
Research Book
Real-Sim Interface: Enabling Multi-resolution Simulation and X-in-the-Loop Development for Connected and Automated Vehicles12-05-04-002606/27/2022
Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory’s (ORNL’s) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network
Shao, Yunli, Deter, Dean, Cook, Adian, Wang, Chieh (Ross), Thompson, Bradley, Perry, Nolan
Journal Article
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202107 (Current)07/16/2021
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users
Cooperative Driving Automation(CDA) Committee
Information Report
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202107-TEST-REC (Historical)07/16/2021
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users
Cooperative Driving Automation(CDA) Committee
Information Report
Taxonomy and Definition of Safety Principles for Automated Driving System (ADS)J3206_202107 (Current)07/07/2021
This SAE Information Report classifies and defines a harmonized set of safety principles intended to be considered by ADS and ADS-equipped vehicle development stakeholders. The set of safety principles herein is based on the collection and analysis of existing information from multiple entities, reflecting the content and spirit of their efforts, including: SAE ITC AVSC Best Practices CAMP Automated Vehicle Research for Enhanced Safety - Final Report RAND Report - Measuring Automated Vehicle Safety: Forging a Framework U.S. DOT: Automated Driving Systems 2.0 - A Vision for Safety Safety First for Automated Driving (SaFAD) UNECE WP29 amendment proposal UNECE/TRANS/WP.29/GRVA/2019/13 On a Formal Model of Safe and Scalable Self-Driving Cars (Intel RSS model) SAE J3018 This SAE Information Report provides guidance for the consideration and application of the safety principles for the development and deployment of ADS and ADS-equipped vehicles. This SAE Information Report is not intended to
On-Road Automated Driving (ORAD) committee
Information Report
Taxonomy and Definition of Safety Principles for Automated Driving System (ADS)J3206_202107-TEST-REC (Historical)07/07/2021
This SAE Information Report classifies and defines a harmonized set of safety principles intended to be considered by ADS and ADS-equipped vehicle development stakeholders. The set of safety principles herein is based on the collection and analysis of existing information from multiple entities, reflecting the content and spirit of their efforts, including: SAE ITC AVSC Best Practices CAMP Automated Vehicle Research for Enhanced Safety - Final Report RAND Report - Measuring Automated Vehicle Safety: Forging a Framework U.S. DOT: Automated Driving Systems 2.0 - A Vision for Safety Safety First for Automated Driving (SaFAD) UNECE WP29 amendment proposal UNECE/TRANS/WP.29/GRVA/2019/13 On a Formal Model of Safe and Scalable Self-Driving Cars (Intel RSS model) SAE J3018 This SAE Information Report provides guidance for the consideration and application of the safety principles for the development and deployment of ADS and ADS-equipped vehicles. This SAE Information Report is not intended to
On-Road Automated Driving (ORAD) committee
Information Report
New SAE Standard for Automated-Driving Developers20AVEP07_0407/01/2020
Developed in less than a year, SAE's new J3216 standard will impact traffic management, operations and safety for automated mobility. In May, amid quarantine and lockdowns for the Coronavirus pandemic, SAE International published SAE J3216 Standard: Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles. The new standard provides clarity to support advancement of full automation. SAE J3216 builds on 2016's SAE J3016 that defines six levels of driving automation, from SAE Level Zero (no automation) to SAE Level 5 (full vehicle autonomy). While J3016 aims to provide clarity and offer a foundation for advancing vehicle automation technologies, the newly-published J3216 does so with terms, definitions and taxonomy focused on Cooperative Driving Automation (CDA), a key building block that supports all levels of automation.
Shuttleworth, Jennifer
Magazine Article
SAE STANDARDS NEWS20AUTP06_0606/01/2020
New Cooperative Driving Automation standard provides clarity to support advancement of full automation In May, amid quarantine and lockdowns for the Coronavirus pandemic, SAE International published SAE J3216 Standard: Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor Vehicles (https://www.sae.org/standards/content/j3216_202005/). The new standard provides clarity to support advancement of full automation. SAE J3216 builds on 2016's SAE J3016 that defines six levels of driving automation, from SAE Level Zero (no automation) to SAE Level 5 (full vehicle autonomy). While J3016 aims to provide clarity and offer a foundation for advancing vehicle automation technologies, the newly-published J3216 does so with terms, definitions and taxonomy focused on Cooperative Driving Automation (CDA), a key building block that supports all levels of automation.
Magazine Article
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202005-TEST-REC (Historical)05/07/2020
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users
Cooperative Driving Automation(CDA) Committee
Information Report
Taxonomy and Definitions for Terms Related to Cooperative Driving Automation for On-Road Motor VehiclesJ3216_202005 (Historical)05/07/2020
This document describes machine-to-machine (M2M) communication to enable cooperation between two or more participating entities or communication devices possessed or controlled by those entities. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle with driving automation feature(s) engaged. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of manually operated vehicles or pedestrians or cyclists carrying personal devices), or road operators (e.g., those who maintain or operate traffic signals or workzones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be used to influence (directly or indirectly) DDT performance by one or more nearby road users
Cooperative Driving Automation(CDA) Committee
Information Report
Continuous Driver Assistance: Technology Leadership Brief2012-01-901410/08/2012
Driver assistance functions are today available in several new vehicle models with clear positive impacts on road safety. But they are covering only specific maneuvers, like for example to keep the vehicle in the lane. To increase such benefits these functions have to cover all possible dangerous scenarios, offering to the driver a “Continuous Support”: an integrated driver assistance system able to support the driver in several different driving situations in a continuous and homogenous way, so that the driver can perceive the driving support functions as a whole. This full support will have great benefit from a “complete” perception of the traffic surrounding the vehicle. Moreover, the perception of the scenario could be not limited only to the field of view of each on board sensor (e.g. radar or camera), but extended thanks to the information available from other vehicles and from the infrastructure: the cooperative systems. These innovative concepts of “continuous driver support
Miglietta, Maurizio, Burzio, Gianfranco, Saroldi, Andrea
Technical Paper
Indirect Lightning Prediction Codes2001-01-289309/11/2001
Codes have been written that allow designers to predict lightning indirect effects on aircraft wiring and structural elements. As a cost saving alternative to aircraft level testing, the codes will be used to calculate actual transient levels (ATL) on aircraft wiring with the purpose of verifying system lightning protection certification compliance by analysis. The code suite employs a CAD feature to enter the problem geometry. Additional user inputs include electrical characteristics of an airframe, excitation of the airframe with lightning attachments, and computation and display of the electrical response of any electrical member of the airframe versus time. The time series for an electrical response is calculated using inverse Laplace transforms of pole-zero terms fitted to the spectral response of the chosen element. In all cases tested to date, very few pole-zero terms are needed compared to the number of frequency samples required for the equivalent accuracy using standard
Nachamkin, Jack, Brigante, Joseph
Technical Paper
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