AI Safety Framework for Autonomous Vehicles
Budget: $30 – $250 USD
This research presents a cutting-edge safety framework designed for autonomous vehicles (AVs), focused on predicting and preventing potential hazards before they occur. By combining artificial intelligence (AI), multi-sensor fusion, and embedded edge intelligence on the Jetson Nano, this system operates in real time without relying on cloud infrastructure.
Current autonomous vehicles mostly rely on reactive systems — meaning they respond after detecting an obstacle or threat. However, this can be too late in high-speed or complex environments. Therefore, a predictive safety system that can foresee dangers a few seconds ahead and act accordingly is essential.
The research proposes a framework that:
• Uses multiple sensors (LIDAR, camera, radar, IMU, GPS) to collect comprehensive environmental data.
• Applies AI models to predict upcoming threats (e.g., collision, object movement, lane deviation).
• Performs all processing on an embedded edge device (Jetson Nano) for low latency and real-time reaction.
This solution ensures fast, smart and reliable decisions that improve passenger safety and system efficiency in autonomous navigation.
Current autonomous vehicles mostly rely on reactive systems — meaning they respond after detecting an obstacle or threat. However, this can be too late in high-speed or complex environments. Therefore, a predictive safety system that can foresee dangers a few seconds ahead and act accordingly is essential.
The research proposes a framework that:
• Uses multiple sensors (LIDAR, camera, radar, IMU, GPS) to collect comprehensive environmental data.
• Applies AI models to predict upcoming threats (e.g., collision, object movement, lane deviation).
• Performs all processing on an embedded edge device (Jetson Nano) for low latency and real-time reaction.
This solution ensures fast, smart and reliable decisions that improve passenger safety and system efficiency in autonomous navigation.