Abstract
The road environment prediction is an essential task for intelligent vehicle. In this study, we provide a flexible system that focuses on freespace detection and road environment prediction to host vehicle. The hardware of this system includes two parts: a binocular camera and a low-power mobile platform, which is flexible and portable for a variety of intelligent vehicle. We put forward a multiscale stereo matching algorithm to reduce the computing cost of the hardware unit. Based on disparity space and points cloud, we propose a weighted probability grid map to detect freespace region and a state model to describe the road environment. The experiments show that the proposed system is accurate and robust, which indicates that this technique is fully competent for road environment prediction for intelligent vehicle.
Highlights
The road environment prediction is an essential task for intelligent vehicle and robotic applications
We propose a new stereo matching framework to adapt to the field-programmable gate array (FPGA) implement environment
(i) A multiscale stereo matching algorithm is presented to reduce the computing cost and improve the accuracy (ii) Based on the disparity map, a weight probability grid map is proposed to detection the freespace region (iii) A state model is proposed to describe the road environment in the front of the host vehicle (iv) An efficient deployment programme is put forward to process our system at the low-power mobile platform in realtime
Summary
The road environment prediction is an essential task for intelligent vehicle and robotic applications. For the sake of real-time system in the low-power platform, we propose a multiscale stereo matching algorithm, weight probability grid map, and state model to describe road environment. We propose a weighted probability grid map to freespace detection It is a robust and flexible strategy because we avoid the motion estimate that is different to match the feature point on static objects. (i) A multiscale stereo matching algorithm is presented to reduce the computing cost and improve the accuracy (ii) Based on the disparity map, a weight probability grid map is proposed to detection the freespace region (iii) A state model is proposed to describe the road environment in the front of the host vehicle (iv) An efficient deployment programme is put forward to process our system at the low-power mobile platform in realtime
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