A Complete Multi-CPU/FPGA-based Design and Prototyping Methodology for Autonomous Vehicles: Multiple Object Detection and Recognition Case Study

Embedded smart systems are Hardware/Software (HW/SW) architectures integrated in new autonomous vehicles in order to increase their smartness. A key example of such applications are camera-based automatic parking systems. In this paper we introduce a fast prototyping perspective within a complete design methodology for these embedded smart systems. One of our main objective being to reduce development and prototyping time, compared to usual simulation approaches. Based on our previous work [1], a supervised machine learning approach, we propose a HW/SW algorithm implementation for objects detection and recognition around autonomous vehicles. We validate our real-time approach via a quick prototype on the top of a Multi-CPU/FPGA platform (ZYNQ). The main contribution of this current work is the definition of a complete design methodology for smart embedded vehicle applications which defines four main parts: specification & native software, hardware acceleration, machine learning software, and the real embedded system prototype. Toward a full automation of our methodology, several steps are already automated and presented in this work. Our hardware acceleration of point cloud-based data processing tasks is 300 times faster than a pure software implementation.