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Now Available from SAE International, SAE OnQue is a revolutionary digital standards solution that optimizes the way automotive and aerospace engineers access standards.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.

In numerous industries such as aerospace and energy, components must perform under significant extreme environments. This imposes stringent requirements on the accuracy with which these components are manufactured and assembled. One such example is the positional tolerance of drilled holes for close clearance applications, as seen in the “EN3201:2008 Aerospace Series – Holes for metric fasteners” standard. In such applications, the drilled holes must be accurate to within ±0.1 mm. Traditionally, this required the use of Computerised Numerical Control (CNC) systems to achieve such tight tolerances. However, with the increasing popularity of robotic arms in machining applications, as well as their relatively lower cost compared to CNC systems, it becomes necessary to assess the ability of robotic arms to achieve such tolerances. This review paper discusses the sources of errors in robotic arm drilling and reviews the current techniques for improving its accuracy.

This document focuses on the latest in-force regulations. However, in addition to latest information, the report may include historical information. As regulations are superseded, the previous entry will remain to help understand the change in requirements over time. The initial focus of the document includes light-, medium-, and heavy-duty on-road vehicles with all propulsion systems. The document will include information from the United States and Canada, with later publications expanding to other regions. Forecasts for future regulations will not be included in the spreadsheet but be kept in a separate document. The document may be expanded to other types of applications/vehicles as information becomes available.

This document is intended for use during audits to the requirements of AS5553C. It may be used by all contracting organizations that procure EEE parts, whether such parts are procured directly or integrated into electronic assemblies or equipment as guidance for evaluating compliance to AS5553C.

The purposeful integration of existing and emerging technologies into CM practice will enable collaboration with supporting systems and provide stakeholders access to authoritative and trusted data in a timely fashion at their desktop to help drive educated decision making. This lays to rest the misguided myth that CM and supporting systems operate at cross-purposes. What does it mean to have CM in a world of new initiatives and 2-week sprints (i.e., time-boxed work periods), multiple increments producing Minimum Viable Products (MVP) and synchronized with Model Based Systems Engineering (MBSE) while being digitally transformed? MBSE initiatives drive the jump from “2D” data to “3D” data, thereby becoming a Model-Centric practice. Products now enable technology to push the product lifecycle management process to new levels of efficiency and confidence. This mindset is evidenced by five major functions of CM, as discussed below, and described in EIA-649C.