1 research outputs found
Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model
This work proposes a new self-driving framework that uses a human driver
control model, whose feature-input values are extracted from images using deep
convolutional neural networks (CNNs). The development of image processing
techniques using CNNs along with accelerated computing hardware has recently
enabled real-time detection of these feature-input values. The use of human
driver models can lead to more "natural" driving behavior of self-driving
vehicles. Specifically, we use the well-known two-point visual driver control
model as the controller, and we use a top-down lane cost map CNN and the YOLOv2
CNN to extract feature-input values. This framework relies exclusively on
inputs from low-cost sensors like a monocular camera and wheel speed sensors.
We experimentally validate the proposed framework on an outdoor track using a
1/5th-scale autonomous vehicle platform