4 Applications of Reconfigurable Computing

17:00 8.4.1 ADAPTIVE VEHICLE DETECTION FOR REAL-TIME AUTONOMOUS DRIVING SYSTEM Speaker: Maryam Hemmati, The University of Auckland, NZ Authors: Maryam Hemmati1, Morteza Biglari-Abhari1 and Smail Niar2 1University of Auckland, NZ; 2University of Valenciennes and Hainaut-Cambresis, FR Abstract Modern cars are being equipped with powerful computational resources for autonomous driving systems (ADS) as one of their major parts to provide safer travels on roads. High accuracy and real-time requirements of ADS are addressed by HW/SW co-design methodology which helps in offloading the computationally intensive tasks to the hardware part. However, the limited hardware resources could be a limiting factor in complicated systems. This paper presents a dynamically reconfigurable system for ADS which is capable of real-time vehicle and pedestrian detection. Our approach employs different methods of vehicle detection in different lighting conditions to achieve better results. A novel deep learning method is presented for detection of vehicles in the dark condition where the road light is very limited or unavailable. We present a partial reconfiguration (PR) controller which accelerates the reconfiguration process on Zynq SoC for seamless detection in real-time applications. By partially reconfiguring the vehicle detection block on Zynq SoC, resource requirements is maintained low enough to allow for the existence of other functionalities of ADS on hardware which could complete their tasks without any interruption. Our presented system is capable of detecting pedestrian and vehicles in different lighting conditions at the rate of 50fps (frames per second) for HDTV (1080x1920) frame.