Real-Time Self-Driving Car Navigation Using Deep Neural Network
Truong-Dong Do, Minh-Thien Duong, Quoc-Vu Dang, My-Ha Le · 2018
In this paper, a monocular vision-based self-driving car prototype using Deep Neural Network on Raspberry Pi is proposed. Self-driving cars are one of the most increasing interests in recent years as the definitely developing relevant hardware and software technologies toward fully autonomous driving capability with no human intervention. Level-3/4 autonomous vehicles are potentially turning into a reality in near future. Convolutional Neural Networks (CNNs) have been shown to achieve significant performance in various perception and control tasks in comparison to other techniques in the latest years. The key factors behind these impressive results are their ability to learn millions of parameters using a large amount of labeled data. In this work, we concentrate on finding a model that directly maps raw input images to a predicted steering angle as output using a deep neural network. The technical contributions of this work are two-fold. First, the CNN model parameters were trained by using data collected from vehicle platform built with a 1/10 scale RC car, Raspberry Pi 3 Model B computer and front-facing camera. The training data were road images paired with the time-synchronized steering angle generated by manually driving. Second, road tests the model on Raspberry to drive itself in the outdoor environment around oval-shaped and 8-shaped with traffic sign lined track. The experimental results demonstrate the effectiveness and robustness of autopilot model in lane keeping task. Vehicle's top speed is about 5-6km/h in a wide variety of driving conditions, regardless of whether lane markings are present or not.