In this paper, we present a distributed embedded vision system that enables surround scene analysis and vehicle threat estimation. The proposed system analyzes the surroundings of the ego-vehicle using four cameras, each connected to a separate embedded processor. Each processor runs a set of optimized vision-based techniques to detect surrounding vehicles, so that the entire system operates at real-time speeds. This setup has been demonstrated on multiple vehicle testbeds with high levels of robustness under real-world driving conditions and is scalable to additional cameras. Finally, we present a detailed evaluation which shows over 95% accuracy and operation at nearly 15 frames per second.
['Frankie Lu', 'Sean Lee', 'Ravi Kumar Satzoda', 'Mohan M. Trivedi']
Embedded Computing Framework for Vision-Based Real-Time Surround Threat Analysis and Driver Assistance