Hierarchical Learned Risk-Aware Planning Framework for Human Driving Modeling

Abstract

This paper presents a novel approach to modeling human driving behavior, designed for use in evaluating autonomous vehicle control systems in a simulation environments. Our methodology leverages a hierarchical forward-looking, risk-aware estimation framework with learned parameters to generate human-like driving trajectories, accommodating multiple driver levels determined by model parameters. This approach is grounded in multimodal trajectory prediction, using a deep neural network with LSTM-based social pooling to predict the trajectories of surrounding vehicles. These trajectories are used to compute forward-looking risk assessments along the ego vehicle’s path, guiding its navigation. Our method aims to replicate human driving behaviors by learning parameters that emulate human decision-making during driving. We ensure that our model exhibits robust generalization capabilities by conducting simulations, employing real-world driving data to
validate the accuracy of our approach in modeling human behavior. The results reveal that our model effectively captures human behavior, showcasing its versatility in modeling human drivers in diverse highway scenarios.