Lane detection (LD) plays an important role in several advanced driver assistance systems (ADASs). In this article, a new solution for LD, dubbed HistWind2, is presented. HistWind2 solution is based on processing image captured by a front view camera and is comprised of three algorithm processing blocks: Image preprocessing block, histogram peak identification block, and sliding window block. Parameters of the algorithms within processing blocks are adjusted using particle swarm optimization on a training dataset, after which the performance of the solution is compared against a freely available and well-known state-of-the-art deep-learning-based solution on a test dataset. The results show that HistWind2 achieves a higher F1-measure and precision while running on a CPU in real time. HistWind2 can be implemented on hardware with limited resources, making it suitable for use in modern vehicles. HistWind2 can process up to 21 frames per second when using 1640×590 pixel frames and running on AMD Ryzen 5 1600 CPU with 24 GB of RAM.