Browse Topic: Driving automation
This SAE Recommended Practice provides guidelines for the use, performance, installation, activation, and switching of marking lamps on Automated Driving System (ADS) equipped vehicles.
This article suggests a validation methodology for autonomous driving. The goal is to validate front camera sensors in advanced driver-assist systems (ADAS) based on virtually generated scenarios. The outcome is the CARLA-based hardware-in-the-loop (HIL) simulation environment (CHASE). It allows the rapid prototyping and validation of the ADAS software. We tested this general approach on a specific experimental application/setup for a vehicle front camera sensor. The setup results were then proven to be comparable to real-world sensor performance. The CARLA simulation environment was used in tandem with a vehicle CAN bus interface. This introduced a significantly improved realism to user-defined test scenarios and their results. The approach benefits from almost unlimited variability of traffic scenarios and the cost-efficient generation of massive testing data.
This specification covers a premium aircraft-quality, low-alloy steel in the form of bars, forgings, mechanical tubing, and forging stock.
It is expected that Level 4 and 5 automated driving systems-dedicated vehicles (ADS-DVs) will eventually enable persons to travel at will who are otherwise unable to obtain a driver’s license for a conventional vehicle, namely, persons with certain visual, cognitive, and/or physical impairments. This information report focuses on these disabilities but also provides guidance for those with other disabilities. This report is limited to fleet-operated, on-demand, shared mobility scenarios, as this is widely considered to be the first way people will be able to interact with ADS-DVs. To be more specific, this report does not address fixed-route transit services or private vehicle ownership. Similarly, this report is focused on motor vehicles (refer to SAE J3016), not scooters, golf carts, etc. Lastly, this report does not address the design of chair lifts, ramps, or securements for persons who use wheeled mobility devices (WHMD) (e.g., wheelchair, electric cart, etc.), as these matters
Mobileye announced in June that its ongoing work with Volkswagen will deliver the automaker's first production SAE Level 4 autonomous vehicles sometime in 2026. The first of these vehicles will be the Volkswagen ID. Buzz AV, which will use the Mobileye Drive autonomous platform and will most likely deploy first in the U.S next year. The ID. Buzz AV is one of four programs Mobileye is working on with VW, Dan Galves, chief communications officer at Mobileye, told SAE Media, and the variety and size of the programs will be key to making AVs scale. The vehicles in each of these programs use the same Mobileye core, with similar cameras and sensors and the same system on chip (SOC), even as the details differ.
We present DISRUPT, a research project to develop a cooperative traffic perception and prediction system based on networked infrastructure and vehicle sensors. Decentralized tracking and prediction algorithms are used to estimate the dynamic state of road users and predict their state in the near future. Compared to centralized approaches, which currently dominate traffic perception, decentralized algorithms offer advantages such as greater flexibility, robustness and scalability. Mobile sensor boxes are used as infrastructure sensors and the locally calculated state estimates are communicated in such a way that they can augment local estimates from other sensor boxes and/or vehicles. In addition, the information is transferred to a cloud that collects the local estimates and provides traffic visualization functionalities. The prediction module then calculates the future dynamic state based on neurocognitive behavior models and a measure of a road user's risk of being involved in
Letter from the Guest Editors
This document describes machine-to-machine (M2M)1 communication to enable cooperation between two or more traffic participants or CDA devices hosted or controlled by said traffic participants. The cooperation supports or enables performance of the dynamic driving task (DDT) for a subject vehicle equipped with an engaged driving automation system feature and a CDA device. Other participants may include other vehicles with driving automation feature(s) engaged, shared road users (e.g., drivers of conventional vehicles or pedestrians or cyclists carrying compatible personal devices), or compatible road operator devices (e.g., those used by personnel who maintain or operate traffic signals or work zones). Cooperative driving automation (CDA) aims to improve the safety and flow of traffic and/or facilitate road operations by supporting the safer and more efficient movement of multiple vehicles in proximity to one another. This is accomplished, for example, by sharing information that can be
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV
While some developers of autonomous technology for commercial trucks have stalled out, there's renewed energy to deliver augmented ADAS and automated driving systems to mass production. After a tumultuous 2023 that saw several autonomous trucking startups pivot out of or exit the arena entirely, there has been a recent resurgence of investment and efforts to bring the vision of driverless freight fleets to reality. In the wake of firms like Embark, TuSimple and Waymo scaling back or rolling up operations, Aurora, Continental and Knorr-Bremse have all announced continued development of SAE Level 4 systems with the intention to deploy trucks using these systems at scale. OEMs such as Volvo Trucks have also announced updates to existing technologies that will augment current advanced driver-assistance systems (ADAS) to help human drivers become safer behind the wheel.
The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain plant features a wetted area of 600 square meters and a sprinkled rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of Radar, Lidar, and Camera
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