  Predicting agents' behavior for vehicles and pedestrians is challenging due
to a myriad of factors including the uncertainty attached to different
intentions, inter-agent interactions, 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.
