Abstract

According to WHO statistics, every year, there are more than a million traffic fatalities world-wide. Self-driving cars and advanced safety features present one of today’s greatest challenges and opportunities for Artificial Intelligence (AI). Despite billions of dollars of investments and encouraging progress under certain operational constraints, there are no driverless cars on public roads today without human safety drivers, and the aforementioned fatalities are still a tragic reality. Autonomous Driving research spans a wide spectrum, from modular architectures – composed of hardcoded or independently learned sub-systems – to end-to-end deep networks with a single model from sensors to controls. In any system, Machine Learning is a key component. However, there are formidable learning challenges due to safety constraints, the need for large-scale manual labeling, and the complex high dimensional structure of driving data, whether inputs (from cameras, HD maps, inertial measurement units, wheel encoders, LiDAR, radar, etc.) or predictions (e.g., world state representations, behavior models, trajectory forecasts, plans, controls). The goal of this workshop is to explore the frontier of learning approaches for safe, robust, and efficient Autonomous Driving (AD) at scale. The workshop will span both theoretical frameworks and practical issues.