ERI: Adaptive Intelligence for Active Safety Control and Privacy-Preserving Autonomous Driving

This Engineering Research Initiation (ERI) grant will support research that will contribute to new knowledge related to collaborative sensing, modeling, and learning to promote active safety control and privacy-preserving autonomous driving. Driving is an essential part of our daily lives and is vulnerable to a range of distractions that can compromise safety on the road.

While internal and external sensing data can be collected and used to improve driving safety, modeling, analyzing, and predicting driver behaviors and attention are challenging due to their dynamic nature and complexity. The data collection and processing also raise concerns about drivers’ privacy, which is under-investigated in the field and can lead to various cybersecurity vulnerabilities.

This project aims to develop a new way to examine and interpret driver behavior and attention to exploit the potential of deep learning-based sensing, reasoning, control, and actuation in a dynamic traffic and driving environment while preserving drivers' privacy. The research can potentially uncover new AI knowledge-based theories and result in a synergistic integration of these new theories with deep learning, prediction, and control.

The results from this project will help enable active safety, promote privacy-preserving autonomous driving, and contribute to the development of smart education and national defense. This project will offer research opportunities to undergraduate and graduate students and provide outreach activities to middle school students to foster the growth of the future science, technology, engineering, and mathematics (STEM) workforce with engineering and AI skills.

This project seeks to develop a data-driven, deep learning-based conceptual framework with knowledge representation and inference rules for capturing, analyzing, and modeling driver behaviors and cognition in real-world driving scenarios. First, driving behaviors and inner connections among behavior, attention, and action will be investigated by exploring limited sensory data while considering privacy protection.

The impacts of human and road environment factors on driving distraction will be assessed. Then, an explainable neural network will be developed to model the driver’s behavior, driving preferences, and distinctive features with knowledge representation and inference rules to predict the driver’s attention and intention. Finally, deep learning techniques will be created to predict the angle and direction of the wheel steering based on actual driver models and perceptions of the surrounding environment.

With successful execution, this project is expected to result in a prototype sensing-intelligence-control-actuation closed-loop system with the proposed theories, models, networks, and techniques. In addition, research findings and knowledge made by this project will be integrated into the Computer Science undergraduate and graduate courses and learning materials of educational and outreach programs to improve public understanding of science and technology and increase their engagement.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.