Software Specifications for Vault ADAS

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Vault ADAS is a software solution designed for Advanced Driver-Assistance Systems (ADAS). It integrates with hardware sensors, data storage, and vehicle control systems to enable features such as collision avoidance, lane-keeping assist, and automated parking. The system is modular, allowing easy expansion with new sensor types and ADAS features. Vault ADAS communicates with various vehicle components in real-time, processing data from cameras, LIDAR, radar, and GPS.

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Key Features:

• Real-Time Sensor Integration: Vault ADAS processes data from sensors (e.g., LIDAR, radar) using libraries such as rosbag for sensor data management or OpenCV for image processing.

• Collision Detection & Avoidance: Uses algorithms like the Rapidly-exploring Random Tree (RRT) for real-time path planning.

• Lane-Keeping Assistance: Implements models such as the LaneNet deep learning model to recognize lane boundaries and provide steering corrections.

• Automated Parking: Integrates with control systems using libraries like Robot Operating System (ROS) to handle car positioning and maneuvering into tight spaces.

• Safety Monitoring: Features driver monitoring through cameras using TensorFlow for facial and eye-tracking analysis to ensure the driver is attentive.

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Example Code Libraries:

• LIDAR Data Processing: pcl (Point Cloud Library) for processing and visualizing LIDAR point clouds.

• Path Planning Algorithms: OMPL (Open Motion Planning Library) for real-time motion planning.

• Machine Learning: scikit-learn for predictive analytics in obstacle detection or TensorFlow for training deep learning models on real-time vehicle data.