Adaptive Real-Time Object Detection for Autonomous Driving Systems

Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.