Janakiraman Bhavithra, Shanmugam Sathiyapriya, Pérez de Prado Rocío, Wozniak Marcin
Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, India.
Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai 600123, India.
Sensors (Basel). 2023 Jun 5;23(11):5358. doi: 10.3390/s23115358.
The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes. This work mainly aims at proposing a new technique with Phase I (road or non-road classification) and Phase II (lane or non-lane classification) with 3D images. Phase I: Initially, the features, such as the proposed local texton XOR pattern (LTXOR), local Gabor binary pattern histogram sequence (LGBPHS), and median ternary pattern (MTP), are derived. These features are subjected to the bidirectional gated recurrent unit (BI-GRU) that detects whether the object is road or non-road. Phase II: Similar features in Phase I are further classified using the optimized BI-GRU, where the weights are chosen optimally via self-improved honey badger optimization (SI-HBO). As a result, the system can be identified, and whether it is lane-related or not. Particularly, the proposed BI-GRU + SI-HBO obtained a higher precision of 0.946 for db 1. Furthermore, the best-case accuracy for the BI-GRU + SI-HBO was 0.928, which was better compared with honey badger optimization. Finally, the development of SI-HBO was proven to be better than the others.
对道路和车道的理解包括识别道路的等级、车道的位置和数量，以及在高速公路、乡村和城市场景中道路和车道的终点、分流和合并情况。尽管最近已经取得了很大进展，但这种理解仍领先于当前感知方法的成果。如今，3D车道检测已成为自动驾驶车辆领域的热门研究，它能够精确估计可行驶车道的3D位置。这项工作主要旨在提出一种新技术，该技术分为两个阶段：第一阶段（道路或非道路分类）和第二阶段（车道或非车道分类），使用3D图像进行。第一阶段：首先，提取诸如提出的局部纹理异或模式（LTXOR）、局部Gabor二值模式直方图序列（LGBPHS）和中值三元模式（MTP）等特征。这些特征被输入到双向门控循环单元（BI-GRU）中，以检测对象是道路还是非道路。第二阶段：使用优化后的BI-GRU对第一阶段的类似特征进行进一步分类，其中权重通过自我改进的蜜獾优化（SI-HBO）进行最优选择。结果，可以识别系统以及它是否与车道相关。特别是，所提出的BI-GRU + SI-HBO在db 1上获得了0.946的更高精度。此外，BI-GRU + SI-HBO的最佳情况准确率为0.928，与蜜獾优化相比表现更好。最后，事实证明SI-HBO的性能优于其他方法。
