Computer vision model based robust lane detection using multiple model adaptive estimation methodology

Lane-keeping systems are a major part of advanced driver assistance systems (ADAS). Existing lane detection algorithms are based on either Computer Vision (CV) models or deep learning techniques which are often vulnerable to unfamiliar routes, lane marking conditions, night-time driving, weather conditions, etc. To improve lane detection accuracy under various challenging conditions, we propose a framework that utilizes several lane detection models with different features to obtain a robust algorithm. The proposed Multiple Model Adaptive Estimation (MMAE) algorithm works with two cameras, one front camera and one rear camera. The front camera is used for lane offset estimates whereas the rear camera serves as a time-delayed reference for the estimated lane offsets. The offsets from front camera CV models (two) are used as inputs to the MMAE algorithm which compares the offset computed by the rear camera CV model (time-delayed) as the reference. The proposed MMAE algorithm then estimates the probability of lane offsets to match the time-delayed reference model lane offset and selects the offset with higher probability of matching with reference model. The offset from the time-delayed reference model cannot be used for the real-time lane keeping control system since it would produce erroneous steering output due to the time lag in offset estimated by the real camera model. Thus, the MMAE estimated offset offers a more accurate lane offset and hence used in a PID steering controller for the lane keeping system. The proposed algorithm is then deployed in an AirSim simulation environment for performance evaluation. The simulation results show that the proposed MMAE algorithm performed robustly even when one of the models performed poorly. The proposed lane detection algorithm was able to identify the poorly performing model and switch to the other model to ensure better lane detection performance.