Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach
This addresses the cost and performance limitations of perception systems in autonomous vehicles, though it appears incremental as it builds on existing sensor fusion techniques.
The paper tackles the problem of generating denser depth maps for autonomous vehicles by integrating radar and camera images, achieving a 27.95% improvement over state-of-the-art methods in Unidirectional Chamfer Distance.
We present a cost-effective new approach for generating denser depth maps for Autonomous Driving (AD) and Autonomous Vehicles (AVs) by integrating the images obtained from deep neural network (DNN) 4D radar detectors with conventional camera RGB images. Our approach introduces a novel pixel positional encoding algorithm inspired by Bartlett's spatial spectrum estimation technique. This algorithm transforms both radar depth maps and RGB images into a unified pixel image subspace called the Spatial Spectrum, facilitating effective learning based on their similarities and differences. Our method effectively leverages high-resolution camera images to train radar depth map generative models, addressing the limitations of conventional radar detectors in complex vehicular environments, thus sharpening the radar output. We develop spectrum estimation algorithms tailored for radar depth maps and RGB images, a comprehensive training framework for data-driven generative models, and a camera-radar deployment scheme for AV operation. Our results demonstrate that our approach also outperforms the state-of-the-art (SOTA) by 27.95% in terms of Unidirectional Chamfer Distance (UCD).