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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.

The automated vehicle industry has been busy designing, developing, and deploying several self driving vehicles and services in the last few years. However, much of the outcomes and the overall outlook of the vehicle and services, such as robotaxis, are not great. Customers and stakeholders complain that the level of automation is low, mostly SAE Levels 1, 2, and very little of Level 3. It appears that Level 4 is far out in the horizon and many wonder if Level 5 is actually achievable.

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.

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.

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.

According to NHTSA, there were 932 vehicle recalls in the United States in 2022, affecting approximately 31 million vehicles; 39 electric vehicle recalls affecting more than 1.3 million vehicles, and 56 ADAS recalls affecting more than 4.7 million vehicles. Furthermore, Warranty Week reports that Worldwide Auto Manufacturers allocated a total of $54.7 billion for future warranty repairs or $670 per vehicle sold in 2022. 2023 Consumer Report indicates that Electrical Vehicles have 79% more reliability issues than ICE vehicles.

This course enables transportation professionals to optimize smart mobility for maximum return within smart cities. It offers a structured introduction to the subjects and makes use of real-life examples, local government, technology solution providers, and consulting. The course integrates insights and understandings related to the best use of technology, best practices, lessons learned, challenges, and opportunities. 

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.

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.

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.

Billions of dollars have been invested in AV trucking. It is no longer a matter of IF, it is a matter of When, Where, Who and How? This will be the most disruptive event to happen in our supply chains in more than 4 decades. Are you ready to help your company usher in the most disruptive technology? This class will help you prepare and understand what you will need to do to become part of the ecosystem. You will learn how to identify what needs to start, stop, and change for you to adopt, integrate, and scale. Join us to learn the answers to key questions like the following: 1)How will maintenance change in the AV trucking ecosystem?

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.

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 ever-increasing networking and automation of vehicles make cybersecurity a core requirement for future vehicles and their components. Automobile manufacturers and suppliers are confronted with new requirements that address the cybersecurity of vehicle IT/OT. This certification, created in partnership between SAE International and TÜV SÜD, provides the necessary basic knowledge for people who work in the field of automotive cybersecurity, management, engineering, and auditing. Course participants will gain an understanding of the current challenges in the automotive industry and the relevance and importance of cybersecurity.

This 4-week virtual-only experience, conducted by leading experts in the autonomous vehicle industry and academia, provides an in-depth look at the most common sensor types used in autonomous vehicle applications. By reviewing the theory, working through examples, viewing sensor data, and programming movement of a turtlebot, you will develop a solid, hands-on understanding of the common sensors and data provided by each. This course consists of asynchronous videos you will work through at your own pace throughout each week, followed by a live-online synchronous experience each Friday. The videos are led by Dr.