EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systems
This work addresses the need for customizable ACC systems that capture drivers' social preferences, though it is incremental as it builds on existing LSTM and Transformer architectures.
The paper tackles the problem of fixed settings in Adaptive Cruise Control (ACC) systems by proposing a data-driven car-following model that allows adjustment of driving discourtesy levels, resulting in reduced Mean Squared Error of spacing and speed compared to baselines on HighD and Waymo datasets.
In the realm of driving technologies, fully autonomous vehicles have not been widely adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing driving experiences. Adaptive Cruise Control (ACC) emerges as a pivotal component of ADAS. However, current ACC systems often employ fixed settings, failing to intuitively capture drivers' social preferences and leading to potential function disengagement. To overcome these limitations, we propose the Editable Behavior Generation (EBG) model, a data-driven car-following model that allows for adjusting driving discourtesy levels. The framework integrates diverse courtesy calculation methods into long short-term memory (LSTM) and Transformer architectures, offering a comprehensive approach to capture nuanced driving dynamics. By integrating various discourtesy values during the training process, our model generates realistic agent trajectories with different levels of courtesy in car-following behavior. Experimental results on the HighD and Waymo datasets showcase a reduction in Mean Squared Error (MSE) of spacing and MSE of speed compared to baselines, establishing style controllability. To the best of our knowledge, this work represents the first data-driven car-following model capable of dynamically adjusting discourtesy levels. Our model provides valuable insights for the development of ACC systems that take into account drivers' social preferences.