  Car-following models have made significant contributions to our understanding
of longitudinal driving behavior. However, they often exhibit limited accuracy
and flexibility, as they cannot fully capture the complexity inherent in
car-following processes, or may falter in unseen scenarios due to their
reliance on confined driving skills present in training data. It is worth
noting that each car-following model possesses its own strengths and weaknesses
depending on specific driving scenarios. Therefore, we propose
EnsembleFollower, a hierarchical planning framework for achieving advanced
human-like car-following. The EnsembleFollower framework involves a high-level
Reinforcement Learning-based agent responsible for judiciously managing
multiple low-level car-following models according to the current state, either
by selecting an appropriate low-level model to perform an action or by
allocating different weights across all low-level components. Moreover, we
propose a jerk-constrained kinematic model for more convincing car-following
simulations. We evaluate the proposed method based on real-world driving data
from the HighD dataset. The experimental results illustrate that
EnsembleFollower yields improved accuracy of human-like behavior and achieves
effectiveness in combining hybrid models, demonstrating that our proposed
framework can handle diverse car-following conditions by leveraging the
strengths of various low-level models.
