Real-Time Ego-Lane Detection in a Low-Cost Embedded Platform using CUDA-Based Implementation

This work assesses the effectiveness of heterogeneous computing based on a CUDA implementation for real-time ego-lane detection using a typical low-cost embedded computer. We propose and evaluate a CUDA-optimized algorithm using a heterogeneous approach based on the extraction of features from an aerial perspective image. The method incorporates well-known algorithms optimized to achieve a very efficient solution with high detection rates and combines techniques to enhance markings and remove noise. The CUDA-based solution is compared to an OpenCV library and to a serial CPU implementation. Practical experiments using TuSimple's image datasets were conducted in an NVIDIA's Jetson Nano embedded computer. The algorithm detects up to 97.9% of the ego lanes with an accuracy of 99.0% in the best-evaluated scenario. Furthermore, the CUDA-optimized method performs at rates greater than 300 fps in the Jetson Nano embedded system, speeding up 25 and 140 times the OpenCV and CPU implementations at the same platform, respectively. These results show that more complex algorithms and solutions can be employed for better detection rates while maintaining real-time requirements in a typical low-power embedded computer using a CUDA implementation.