A synchronous multi-modal data acquisition and processing system for ADAS applications using an onboard GPU

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Department of Applied Computing


We present a cost-effective testbed and advanced software architecture suitable for ADAS applications, which utilizes an onboard GPU for high-performance processing. By implementing an optimized parallel processing method, this innovative architecture can collect and synchronize multi-sensor data, such as camera and radar, with high precision. The software architecture dynamically allocates processing power to each data processing sub-module based on the utilized sensor hardware and optimizes the data flow accordingly. With the aid of parallel processing, the system can effectively reduce the time delay during data acquisition. Indoor and road stereo-camera and dual-radar data acquisition results are presented. Post-processing of the data includes the creation of disparity map depth images and combined radar point clouds identifying departing and approaching vehicles and objects.

Publication Title

Proceedings of SPIE, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023