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.
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This content will become publicly available on September 24, 2024
Inverse Reinforcement Learning and Gaussian Process Regression-based Real-Time Framework for Personalized Adaptive Cruise Control
Adaptive Cruise Control (ACC) has become increasingly popular in modern vehicles, providing enhanced driving safety, comfort, and fuel efficiency. However, predefined ACC settings may not always align with a driver's preferences, leading to discomfort and possible safety hazards. To address this issue, Personalized ACC (P-ACC) has been studied by scholars. However, existing research mostly relies on historical driving data to imitate driver styles, which ignores real-time feedback from the driver. To overcome this limitation, we propose a cloud-vehicle collaborative P-ACC framework, which integrates real-time driver feedback adaptation. This framework consists of offline and online modules. The offline module records the driver's naturalistic car-following trajectory and uses inverse reinforcement learning (IRL) to train the model on the cloud. The online module utilizes the driver's real-time feedback to update the driving gap preference in real-time using Gaussian process regression (GPR). By retraining the model on the cloud with the driver's takeover trajectories, our approach achieves incremental learning to better match the driver's preference. In human-in-the-loop (HuiL) simulation experiments, the proposed framework results in a significant reduction of driver intervention in automatic control systems, up to 70.9%.
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- Award ID(s):
- 2152258
- NSF-PAR ID:
- 10510973
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-9946-2
- Page Range / eLocation ID:
- 4428 to 4435
- Format(s):
- Medium: X
- Location:
- Bilbao, Spain
- Sponsoring Org:
- National Science Foundation
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