# Real-time Learning of Driving Gap Preference for Personalized Adaptive Cruise Control

arXiv: https://arxiv.org/abs/2309.05115 · HTML: https://mlsanity.com/paper/2309.05115/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 10 Sep 2023.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 1 of 1)

- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)

## Built on by (most cited first, 3 of 3)

- [Interactive and Hybrid Imitation Learning: Provably Beating Behavior Cloning](https://mlsanity.com/paper/2412.07057/) (2024)
- [Your Ride, Your Rules: Psychology and Cognition Enabled Automated Driving Systems](https://mlsanity.com/paper/2506.11842/) (2025)
- [Learning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking](https://mlsanity.com/paper/2609.37684/) (2026)

## Abstract (arXiv)

Advanced Driver Assistance Systems (ADAS) are increasingly important in improving driving safety and comfort, with Adaptive Cruise Control (ACC) being one of the most widely used. However, pre-defined ACC settings may not always align with driver's preferences and habits, leading to discomfort and potential safety issues. Personalized ACC (P-ACC) has been proposed to address this problem, but most existing research uses historical driving data to imitate behaviors that conform to driver preferences, neglecting real-time driver feedback. To bridge this gap, we propose a cloud-vehicle collaborative P-ACC framework that incorporates driver feedback adaptation in real time. The framework is divided into offline and online parts. The offline component records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. In the online component, driver feedback is used to update the driving gap preference in real time. The model is then retrained on the cloud with driver's takeover trajectories, achieving incremental learning to better match driver's preference. Human-in-the-loop (HuiL) simulation experiments demonstrate that our proposed method significantly reduces driver intervention in automatic control systems by up to 62.8%. By incorporating real-time driver feedback, our approach enhances the comfort and safety of P-ACC, providing a personalized and adaptable driving experience.