DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network (DNN) based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called Dave2 by NVIDIA, which drove on public roads using a deep convolutional neural network (CNN), that takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the exact same network architecture—9 layers, 27 million connections and 250K parameters—and can be trained to drive itself, in real-time, using a web camera and a modest Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar’s CNN based real-time control software as a workload. We find all tested platforms, including the Pi 3, are capable of supporting deep-learning based real-time control, from 20 Hz up to 100 Hz depending on hardware platform. However, shared resource contention remains an important issue that must be considered in applying deeplearning models on shared memory based embedded computing platforms.