Hierarchical Learned Risk-Aware Planning Framework for Human Driving Modeling

TL;DR

This paper introduces a hierarchical, risk-aware modeling framework for human driving behavior that uses deep learning to generate realistic trajectories and assess risks, aiding autonomous vehicle evaluation.

Contribution

It presents a novel hierarchical, risk-aware estimation framework with learned parameters for human-like trajectory prediction in driving scenarios.

Findings

Effectively captures human driving behavior in diverse highway scenarios.

Demonstrates robust generalization using real-world driving data.

Utilizes deep neural networks with LSTM social pooling for trajectory prediction.

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.