Expert Driver

Expert driver modeling aims to replicate the decision-making and driving behavior of skilled human drivers for autonomous vehicle development. Current research focuses on improving the robustness and generalizability of autonomous driving systems through techniques like imitation learning, often enhanced by data augmentation methods such as counterfactual explanations and the use of graph-based prediction and planning networks. These advancements leverage diverse data sources, including real-world driving data, virtual reality simulations, and even commodity vision data, to train models that can handle complex and unpredictable driving scenarios, ultimately contributing to safer and more reliable autonomous vehicles.