Vision-Based Autonomous Vehicle Control using the Two-Point Visual Driver Control Model
This work addresses self-driving vehicle control for improved naturalness, but it is incremental as it adapts existing models to new sensor inputs.
The paper tackles autonomous vehicle control by using a human driver model with features extracted from images via CNNs, achieving natural driving behavior validated on an outdoor track with a 1/5th-scale vehicle.
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.