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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.

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 class will provide the student with the skills, knowledge, and abilities to interpret, analyze and apply HVEDR data in real-world applications. This course has been designed to build on the concepts presented in the SAE course Accessing and Interpreting Heavy Vehicle Event Data Recorders (ID# C1022). Advanced topics will include associating HVEDR data with collision events through timestamps, odometer logs, and data signatures, validating HVEDR speed data using specified vehicle parameters, performing time and distance analyses using HVEDR data, and correlating HVEDR data to physical evidence from the vehicle and roadway.

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 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).

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. 

Looking for a solution that will provide a deeper understanding of the autonomous vehicle (AV) space for your employees? Hoping to offer training as a value add for employee retention? Need a flexible option online that can be taken anywhere, anytime? SAE’s Autonomous Vehicles for Transportation Professionals is the answer. The instructor, Dr. Rahul Razdan, is a leading authority in the field with extensive background in computing from his work at Carnegie-Mellon and Harvard Universities. He has written extensively on these topics at SAE with the landmark SAE Edge reports and on his Forbes transportation column.

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.

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.

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.

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 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 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.