Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

不同照明条件下的车道检测（LD）是车道偏离警告系统和车辆定位的重要组成部分，这是未来智慧城市的当前趋势。最近，提出了基于视觉的方法来检测包括异常标志物情况在内的不同道路情况下的车道标志。但是，尚未引入无人驾驶汽车的包容性框架。在这项工作中，在基于物联网的框架（IBF）中为自动驾驶汽车提出了一种新颖的LD和跟踪方法。IBF由三个模块组成，分别是车载板（VB），云模块（CM）和车载遥控器。LD和跟踪首先由VB执行，然后在发生任何故障的情况下，将整个数据集传递给CM进行处理，然后将结果发送给VB以执行适当的操作。如果CM检测到车道偏离，则将自动驾驶自动驾驶车辆，并重新启动VB。除了提出的框架，还提出了一种照明不变性方法来检测不同光照条件下的车道标记。带有实际数据的模拟结果表明，在隧道和高速公路上的车道保持率分别为95.3％和95.2％。所提出的方法的大约处理时间为31 ms /帧，可以满足实时性要求。隧道和公路分别占2％。所提出的方法的大约处理时间为31 ms /帧，可以满足实时性要求。隧道和公路分别占2％。所提出的方法的大约处理时间为31 ms /帧，可以满足实时性要求。