School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2023 Apr 18;23(8):4085. doi: 10.3390/s23084085.
Lane detection in driving situations is a critical module for advanced driver assistance systems (ADASs) and automated cars. Many advanced lane detection algorithms have been presented in recent years. However, most approaches rely on recognising the lane from a single or several images, which often results in poor performance when dealing with extreme scenarios such as intense shadow, severe mark degradation, severe vehicle occlusion, and so on. This paper proposes an integration of steady-state dynamic equations and Model Predictive Control-Preview Capability (MPC-PC) strategy to find key parameters of the lane detection algorithm for automated cars while driving on clothoid-form roads (structured and unstructured roads) to tackle issues such as the poor detection accuracy of lane identification and tracking in occlusion (e.g., rain) and different light conditions (e.g., night vs. daytime). First, the MPC preview capability plan is designed and applied in order to maintain the vehicle on the target lane. Second, as an input to the lane detection method, the key parameters such as yaw angle, sideslip, and steering angle are calculated using a steady-state dynamic and motion equations. The developed algorithm is tested with a primary (own dataset) and a secondary dataset (publicly available dataset) in a simulation environment. With our proposed approach, the mean detection accuracy varies from 98.7% to 99%, and the detection time ranges from 20 to 22 ms under various driving circumstances. Comparison of our proposed algorithm's performance with other existing approaches shows that the proposed algorithm has good comprehensive recognition performance in the different dataset, thus indicating desirable accuracy and adaptability. The suggested approach will help advance intelligent-vehicle lane identification and tracking and help to increase intelligent-vehicle driving safety.
车道检测在驾驶环境中是高级驾驶辅助系统（ADAS）和自动驾驶汽车的关键模块。近年来已经提出了许多先进的车道检测算法。然而，大多数方法都依赖于从单个或多个图像中识别车道，这在处理极端情况（如强烈的阴影、严重的标记退化、严重的车辆遮挡等）时往往会导致性能不佳。本文提出了一种将稳态动力学方程和模型预测控制-预览能力（MPC-PC）策略相结合的方法，用于找到自动驾驶汽车在 clothoid 形式道路（结构化和非结构化道路）上行驶时的车道检测算法的关键参数，以解决车道识别和跟踪在遮挡（例如，雨）和不同光照条件（例如，白天和夜间）下检测精度差的问题。首先，设计并应用了 MPC 预览能力计划，以保持车辆在目标车道上。其次，作为车道检测方法的输入，使用稳态动力学和运动方程计算偏航角、侧滑和转向角等关键参数。所开发的算法在模拟环境中使用主要数据集（自己的数据集）和次要数据集（公共数据集）进行了测试。使用我们提出的方法，在各种驾驶情况下，平均检测精度从 98.7%到 99%不等，检测时间从 20 到 22 毫秒不等。与其他现有方法相比，我们提出的算法的性能比较表明，该算法在不同数据集上具有良好的综合识别性能，因此具有良好的准确性和适应性。所提出的方法将有助于推进智能车辆的车道识别和跟踪，并有助于提高智能车辆的驾驶安全性。
