Connected Vehicle/Testing End Public 'Shadow' Driving! by Michael DeKort The best way to test and train AI for autonomous vehicles is through proper simulation, systems engineering, and an endstate scenario matrix. A veteran engineer explains why the current AV testing paradigm must change. In critical developmentdriving scenarios it is impossible to provide the 'shadow' driver with a sufficient margin of time to regain situational awareness for safely executing effective vehicle control. 18 January 2019 Lindsay Brooke Autonomous vehicle developers are widely using public "shadow" driving which involves a human in the driver's seat letting go of the steering wheel and ceding control to the system under test to observe how it performs. The fundamental premise of this process is that the vehicle has learned the proper management of possible events which may occur during the maneuvers, and the human observer can react fast enough to stop any negative results from occurring. It is a myth that public shadow driving is the best or only solution to create a fully autonomous vehicle, for several reasons. To complete such an effort would require each AV maker to accumulate roughly one trillion miles in driving and redriving all the potential scenarios. The estimated cost of such programs is over $300 billion [based on the author's conservative calculation of 234,000 vehicles operating at an average of 50 mph, every day all day for 10 years, to arrive at one trillion miles]. Other problems with shadow driving involve safety, including the running of actual accident scenarios to train the AI and SAE Level 3/handover. The process of accident-scenario "training" has potential to cause thousands of accidents, injuries and casualties when efforts to train and test the AI move from the benign scenarios to more complex and dangerous ones. Thousands of accident scenarios will have to be driven multiple times on the public streets Then there's the issue of vehicle-control handover. In critical driving scenarios-whether in a system under development using public shadow driving or AUTONOMOUS VEHICLE ENGINEERING