NeuroDrive

Inspiration

Randomly, we encountered an unexpected statistic: 1 in 25 adults has dozed off while driving in the previous month. This made us discover that fatigue-related driving is responsible for more than 100,000 accidents annually in the U.S., with a significant number involving long-haul operators. We were amazed at how frequently fatigue and distraction remain overlooked until it's too late — particularly on lengthy journeys. That’s when we started considering if we could create a system that not only comprehends the road but also focuses on the driver. That concept formed the basis for NeuroGuardian.

What it does

Data Entries An EEG headset was utilized to track the driver’s cognitive condition in real time, recording signals indicative of attention and fatigue. In addition, we installed a dual-camera system: one directed at the driver to monitor behavior, and another aimed at the road to collect traffic and lane information. Detection Workflow We incorporated YOLOv8 for identifying objects and utilized UltraFast Lane Detection to analyze the drivable region. These outputs were merged to categorize objects according to the lane zone where they were detected. At the same time, EEG signals were examined to identify states of distraction or fatigue. Results & Notifications If the system identifies distraction or potential risk in the driver's lane, it activates a voice alert through a text-to-speech system (using Gemini). The alert aims to be simple yet impactful, assisting in redirecting the driver’s attention without causing them to feel overloaded

How we built it

NeuroGuardian is an assistant for driver awareness that integrates brainwave signals with computer vision to minimize distraction and fatigue. An EEG headset tracks the driver's concentration in real time, supplemented by a dual-camera system—one directed at the road and the other observing the driver. It identifies lane boundaries and detects objects with YOLOv8, subsequently classifying those objects according to their location in relation to the driving lane. If it senses a lapse in attention or a possible danger in a crucial area, it provides a brief voice alert via a text-to-speech system to redirect the driver’s focus to the road.

Challenges we ran into

We faced issues with training and fine-tuning models due to limited processing power. Additionally, finding data sets for our EEG model was difficult.

Accomplishments that we're proud of

We are proud of combining both EEG technology and CV to protect drivers, which hasn't been done before.

What we learned

We learned how to reduce the noise in data. The data and dataset for all our AI models were noisy, so we've learned the importance and a lot of techniques in it while tackling the data analysis.

What's next for NeuroDrive

We would like to use a smaller BCI device such as Muse2, but the data quality is low, so we also need to improve the machine learning model. We will also need feedback from the users so that we can find further improvements.