Isele, David (University of Pennsylvania, Honda Research Institute USA), Gupta, Piyush (TriCom Quest), Liu, Xinyi (Carnegie Mellon University), Bae, Sangjae (Honda Research Institute, USA)

Gaussian Lane Keeping: A Robust Prediction Baseline

Scheduled for presentation during the Poster Session "Operation and navigation of automated vehicles" (FrAT16), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

Abstract

Predicting agents’ behavior for vehicles and pedes- trians is challenging due to a myriad of factors including the uncertainty attached to different intentions, inter-agent inter- actions, traffic (environment) rules, individual inclinations, and agent dynamics. Consequently, a plethora of neural network- driven prediction models have been introduced in the literature to encompass these intricacies to accurately predict the agent behavior. Nevertheless, many of these approaches falter when confronted with scenarios beyond their training datasets, and lack interpretability, raising concerns about their suitability for real-world applications such as autonomous driving. Moreover, these models frequently demand additional training, substantial computational resources, or specific input features necessitating extensive implementation endeavors. In response, we propose Gaussian Lane Keeping (GLK), a robust prediction method for autonomous vehicles that can provide a solid baseline for comparison when developing new algorithms and a sanity check for real-world deployment. We provide several extensions to the GLK model, evaluate it on the CitySim dataset, and show that it outperforms the neural-network based predictions.