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This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. Problems arise in all facets of life. This is particularly true in the business world. Problems impact all aspects of an organization’s operations such as; product development, product quality, process efficiency, marketing efforts, human relations, leadership efforts, financial execution and ultimate profitability. If these problems are not addressed, they inhibit the operation of the business and can eventually result in total failure.

There is a growing interest in the concept of a smart city and how these advanced technologies will improve the quality of living and make a city more attractive to visitors, commerce and industry. This course fills an unmet need for defining and explaining the relationship between connected and autonomous vehicles (CAVs) and smart city transportation. It is apparent that CAVs will achieve the best results when integrated with current and emerging urban infrastructure for transportation. This course addresses such integration from technology, organizational, policy and business model perspectives.

This course is offered in China only and presented in Mandarin Chinese. Driving simulator is a key component of automotive interactive design, which can provide objective data to support both the effectiveness of design and user experience. On the other hand, driving simulator and the design of its experiments are major challenges, as improper experiment design will lead to consequences such as failure in identifying designing errors of the interactive design itself.

There is been tremendous progress in the application of technology and artificial intelligence to connected and autonomous vehicles. At the same time, there have been considerable advances in data science and data analysis that allows large data sets to be managed for results. This course introduces big data and analytics, focusing on how these will be applied to data generated by autonomous and connected vehicles. These technologies will be explained within the context of a smart city.

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Light detection and ranging (LIDAR) sensing, a sensing method that detects objects and maps their distances, is seeing rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve. This course will provide the foundation to build LIDAR technologies in automotive applications.

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Among the challenges are those of sensing the environment in and around the vehicle. Infrared camera sensing is seeing a rapid growth and adoption in the industry. The applications and illumination architecture options continue to evolve. This course will provide the foundation on which to build near infrared camera technologies for automotive applications.

课程概述 Powertrain controls for NEVs are one of the most complex and highly confidential areas of NEV research and development.  This two-day course takes the seemingly complicated field of NEV powertrain controls and summarizes it into a few basic principles.  The latest and most popular NEV powertrains are also reviewed to illustrate these principles and the controls strategies used.  对于新能源汽车来说，动力总成控制一直以来都是最复杂的和高度机密的领域之一。在这两天的课程中，我们将把看似复杂的动力总成控制系统总结出几条基本规则，同时，通过对当今其他车型动力控制系统的案例分析，来把这些规则和原理进行融会贯通。

The ability to write concise and unambiguous reports, proposals, manuals, or other technical documents is a key skill for any high-functioning engineer or technical staff person in the mobility industries. Through a combination of class discussions, interactive workshop activities, assignments, checker teams (review teams) and job aids, this course delivers real-life technical writing techniques and tools that can be immediately applied. Participants discover the importance of knowing their audiences and how to communicate technical information in a 'user-friendly' style.

This course provides an overview of state-of-the-art intelligent vehicles, presents a systematic framework for intelligent technologies and vehicle-level architecture, and introduces testing methodologies to evaluate individual and integrated intelligent functions. Considering the increasing demand for vehicle intelligence, it is critical to gain an understanding of the growing variety of intelligent vehicle technologies and how they must function together effectively as a system.

This course examines ADAS and autonomous vehicle technologies that offer the potential to increase safety while attempting to optimize the cost of car ownership. LIDAR (light detection ranging) and Infrared camera sensing are seeing a rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve almost every six months. This course will provide the foundation to build on for these two technologies in automotive applications. It will include a demonstration model for LIDAR and Infrared camera.

This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. This course serves a dual purpose: it delves into fundamental DFMEA principles and their practical applications while also offering guidance on leading DFMEA teams. Participants will be introduced to crucial FMEA concepts, along with the theoretical foundations before exploring how to implement these concepts in their DFMEA endeavors. Often, the FMEA process can become a mere replication of past efforts, which poses risks for both organizations developing the products under scrutiny and the end-users.

This 4-week virtual-only experience is conducted by leading experts in the autonomous vehicle industry and academia. You’ll develop an understanding of the fundamentals of AV architecture, including mechatronics, kinematics, and the sense-think-act framework in autonomous systems. The course builds a connection for how robotics are used in autonomous vehicles and provides you with demonstrations, procedures, and the skills necessary to program a robot with basic commands using the Robot Operating System (ROS).

Vehicle cybersecurity vulnerabilities could impact a vehicle's safe operation. Therefore, engineers should ensure that systems are designed free of unreasonable risks to motor vehicle safety, including those that may result due to existence of potential cybersecurity vulnerabilities. The automotive industry is making vehicle cybersecurity an organizational priority.

Active safety and (ADAS) are now being introduced to the marketplace as they serve as key enablers for anticipated autonomous driving systems. Automatic emergency braking (AEB) is one ADAS application which is either in the marketplace presently or under development as nearly all automakers have pledged to offer this technology by the year 2022. This one-day course is designed to provide an overview of the typical ADAS AEB system from multiple perspectives.

Crash reconstruction is a scientific process that utilizes principles of physics and empirical data to analyze the physical, electronic, video, audio, and testimonial evidence from a crash to determine how and why the crash occurred. This course will introduce this reconstruction process as it gets applied to various crash types - in-line and intersection collisions, pedestrian collisions, motorcycle crashes, rollover crashes, and heavy truck crashes. Methods of evidence documentation will be covered. Analysis methods will also be presented for electronic data from event data recorders and for video.

Photographs and video recordings of vehicle crashes and accident sites are more prevalent than ever, with dash mounted cameras, surveillance footage, and personal cell phones now ubiquitous. The information contained in these pictures and videos provide critical information to understanding how crashes occurred, and  analyze physical evidence. This course teaches the theory and techniques for getting the most out of digital media, including correctly processing raw video and photographs, correcting for lens distortion, and using photogrammetric techniques to convert the information in digital media to usable scaled three-dimensional data.

This course will introduce participants to industry best practices for real-world aviation cyber-security risk-assessment, development & assurance. Participants will learn the information necessary to help minimize DO-326/ED-202-set compliance risks and costs, while also optimizing cyber-security levels for the development, deployment and in-service phases Topics such as aircraft security aspects of safety, systems-approach to security, security planning, the airworthiness security process, and security effectiveness assurance will be covered.

The cost of this introductory course can be applied to the cost of the full courses: C2215, Safety Management Systems for Design, Manufacturing and Maintenance Providers in Aviation C2216, Safety Risk Management and Safety Assurance for Design, Manufacturing and Maintenance Providers in Aviation Historically, organizations tend to be punitive and focused on who to blame when an unwanted event occurs. Investigations can begin with the intent to blame and discipline which leads to adversarial relationships between management and employees. 

Convolutional neural networks are the de facto method of processing camera, radar, and lidar data for use in perception in ADAS and L4 vehicles, yet their operation is a black box to many engineers. Unlike traditional rules-based approaches to coding intelligent systems, networks are trained and the internal structure created during the training process is too complex to be understood by humans, yet in operation networks are able to classify objects of interest at error rates better than rates achieved by humans viewing the same input data.

The course will enable the learner to apply the fundamental principles behind safety of machine learning to a wide range of applications. The course guides learners through an appropriate selection of methods and tools tailored to the learner’s specific projects. With the acquired knowledge the learner will be able to shape the development and assessment of ML-based safety-related functions enabling their teams to leverage the power of advanced ML techniques without undermining safety.