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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 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.

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. 

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 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 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.

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.

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.

This course highlights the technologies enabling ADAS and how they integrate with existing passive occupant crash protection systems, how ADAS functions perceive the world, make decisions, and either warn drivers or actively intervene in controlling the vehicle to avoid or mitigate crashes. Examples of current and future ADAS functions, and various sensors utilized in ADAS, including their operation and limitations, and sample algorithms, will be discussed and demonstrated. The course utilizes a combination of hands-on activities, including computer simulations, discussion and lecture.

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).

"Spotlight on Design" features video interviews and case study segments, focusing on the latest technology breakthroughs. Viewers are virtually taken to labs and research centers to learn how design engineers are enhancing product performance/reliability, reducing cost, improving quality, safety or environmental impact, and achieving regulatory compliance. In the episode "Automated Vehicles: Sensors and Future Technologies" (24:31), highly automated driving is looked at in detail as the culmination of years of research in automotive technology, sensors, infrastructure, software, and systems integration. Real-life case studies show how organizations are actually developing solutions to the challenge of making cars safer with less driver intervention. IAV Automotive Engineering demonstrates how a highly automated vehicle capable of lane changing was created.

"Spotlight on Design: Insight" features an in-depth look at the latest technology breakthroughs impacting mobility. Viewers are virtually taken to labs and research centers to learn how design engineers are enhancing product performance/reliability, reducing cost, improving quality, safety or environmental impact, and achieving regulatory compliance. Automated driving is made possible through the data acquisition and processing of many different kinds of sensors working in unison. Sensors, cameras, radar, and lidar must work cohesively together to safely provide automated features. In the episode "Automated Vehicles: Converging Sensor Data" (8:01), engineers from IAV Automotive Engineering discuss the challenges associated with the sensor data fusion, and one of Continental North America’s technical teams demonstrate how sensors, radars, and safety systems converge to enable higher levels of automated driving.

Auto manufacturers have known and surveys confirm that consumers require short payback periods (2-4 years) for investments in fuel economy. Using societal discount rates, engineering-economic generally find substantial potential to increase fuel economy, cost-effectively. This phenomenon, often referred to as the ?energy paradox?, has been observed in nearly all consumers? choices of energy-using durable goods. Loss aversion, perhaps the most well established theory of behavioral economics, provides a compelling explanation. Engineering economic analyses generally overlook the fact that consumers? investments in fuel economy are not sure things but rather risky bets. Future energy prices, real world on-road fuel economy, and many other factors are uncertain. Loss aversion describes a fundamental human tendency to exaggerate the potential for loss relative to gain when faced with a risky bet. It provides a sufficient explanation for consumers?

Moir� method is useful to measure the shape and the whole-field distributions of displacement and strain of structures. There are many kinds of moir� methods such as geometric moir� method, sampling moir� method, Fourier transform moir� method, moir� interferometry, shadow moir� method and moir� topography. Grating method analyzing directly deformation of a grating without any moir� fringe pattern is considered as an extended technique of moire method. Phase analysis of the moire fringe patterns and the grating patterns provides accurate measurements of shapes or displacement and strain distributions. Some applications of these moir� methods and grating methods to dynamic shape and strain distribution measurements of a rotating tire, sub-millimeter displacement measurements from long distance for landslide prediction, real-time shape measurements with micro-meter order accuracy, etc. are shown. Presenter Yoshiharu Morimoto, Moire Institute Inc.

Learn how the U.S. Department of Energy's Clean Cities Program can help with the deployment of alternative fuel and advanced technology school buses. Presenter Mark S. Smith, US Department of Energy

Propane autogas, the world?s third most-used engine fuel, powers vehicles, transit buses, and now school buses. Blue Bird has recently launched the Next Generation Vision type C school bus, powered by a ROUSH CleanTech liquid propane autogas fuel system and a Ford 6.8L V10 engine. The bus reduces operating costs by up to 40%, greenhouse gas emissions by up to 24%, and maintains the factory horsepower, torque, and towing capacity ratings. Learn about how school districts are saving over $.30 / mile using this clean, domestically-produced fuel. Presenter Brian Carney, Roush CleanTech.