Introduction to Highly Automated Vehicles
Every year, the U.S. on average, experiences more than 34,000 traffic deaths and over 5 million vehicle crashes. While the trend in traffic deaths has been generally downward for the past decade, most of this reduction has been the result of optimizing passive occupant crash protection systems such as seatbelts and airbags. Highly automated vehicle's (HAV's) offer the potential to significantly reduce vehicle crashes by perceiving a dangerous situation before the crash has occurred and supporting the human driver with proactive warnings and in some cases active interventions to avoid or mitigate the crash. Fully autonomous vehicles promise even greater benefits, such as increased mobility for elderly, visually-impaired, and other physically challenged individuals, reduced public infrastructure needs such as parking decks, and reduced environmental impact.
This course is designed to familiarize participants with the technologies enabling highly automated vehicles, and how they integrate with existing passive occupant crash protection systems. You will learn how HAV's 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 HAV functions, various sensors used, including their operation and limitations, and sample algorithms, will be discussed and demonstrated. The course also looks at the ethics driving HAV behavior, liability considerations and reviews the current and future regulatory landscape. The course uses a combination of lectures, class discussions, computer simulations, and videos.
What Will You Learn
- Explain the SAE Levels of Automation and where different HAV functions fit in the hierarchy
- Explain the HAV functions and articulate their limitations
- Identify different sensors used in HAV systems, how they operate, and their limitations
- Analyze how different sensors can be combined to improve overall system performance
- Describe the current and future methodologies used in developing HAV algorithms
- Articulate how ROC curves, DOE and Monte Carlo techniques can be used to measure and improve algorithm performance
- Critically examine the proposed federal rules and validation methods for HAV systems
- Analyze how HAV\'s may affect the performance of existing passive occupant safety systems and how integration of the systems might be accomplished
- Describe liability and policy considerations for OEM\'s and Tier suppliers working on HAV technologies
Is This Course For You
This course is designed for all professionals - technical or managerial - who are involved either directly or indirectly with vehicle safety performance. Professionals in legal and regulatory and compliance areas concerned with proposed NHTSA rulemaking, and insurance industry analysts developing coverage standards for vehicles with active safety technologies will also find this course useful.