Explore an invited talk from the GPU Technology Conference (GTC) 2018 that delves into learning steering bounds for parallel autonomy and handling ambiguity in end-to-end driving. Discover the latest methodologies for training end-to-end systems in autonomous vehicles, focusing on the challenges of integrating decision-making capabilities beyond reactionary control. Examine the concept of parallel autonomy, its architecture, and hardware implementation. Investigate approaches to learning steering distributions, including discrete action learning and multimodal distributions. Gain insights into dataset collection, Bayesian deep learning, and uncertainty estimation in end-to-end steering control. Analyze the advantages and limitations of these approaches in the context of autonomous driving pipelines and higher-level decision making.