Tech Support - DriveSafe

Tech Support

As the developers of DriveSafe, we are excited to share our AI-powered application designed to enhance road safety by combating driver fatigue—an issue particularly significant in Alberta with its long highways and harsh winters. DriveSafe continuously monitors driver alertness by integrating neurotechnology and AI, utilizing EEG data from the Muse S device and real-time facial analysis through a camera feed. We built the app with a React frontend and a Flask backend to efficiently process live data. The Muse S device captures EEG signals, which we filter to assess concentration and fatigue levels. Simultaneously, OpenCV analyzes facial landmarks to detect signs of drowsiness like eye closure and yawning. When decreased alertness is detected, the system provides real-time voice alerts to help drivers refocus. To offer a comprehensive safety approach, we integrated a weather API to account for external conditions that might impact driver alertness. We store historical data in an SQLite database, allowing for weekly trend analysis visualized with Matplotlib. This helps drivers understand and improve their driving habits over time. We overcame challenges in integrating multiple technologies and optimizing real-time data processing to ensure low latency and reliable alerts. Looking ahead, we plan to integrate DriveSafe with electric vehicles like Tesla. This integration would enable the vehicle to automatically switch to self-driving mode when critical drowsiness levels are detected, enhancing safety by allowing the car to guide itself to the roadside. Our ultimate goal is to expand this feature to other electric vehicles, leveraging AI-driven interventions to improve driver safety across autonomous platforms. By combining neurotechnology, AI, and real-time data analysis, DriveSafe acts as a smart co-pilot, proactively detecting fatigue and assisting drivers to stay alert, making roads safer for everyone.