Machine Learning-Based Lane Detection and Lateral Offset Estimation Model for Vehicle Following Applications

Precisely understanding the driving environment and determining the vehicle’s accurate position is crucial for a safe automated maneuver. vehicle following systems that offer higher energy efficiency by precisely following a lead vehicle, the relative position of the ego vehicle to lane center is a key measure to a safe automated speed and steering control. This article presents a novel Enhanced Lane Detection technique with centimeter-level accuracy in estimating the vehicle offset from the lane center using the front-facing camera. Leveraging state-of-the-art computer vision models, the Enhanced Lane Detection technique utilizes YOLOv8 image segmentation, trained on a diverse world driving scenarios dataset, to detect the driving lane. To measure the vehicle lateral offset, our model introduces a novel calibration method using nine reference markers aligned with the vehicle perspective and converts the lane offset from image coordinates to world measurements. This design minimizes the sensitivity of offset estimation to lane detection accuracy and vehicle orientation. Compared to the existing deep learning-based depth perception models and stereo vision systems, our calibration method significantly improves postprocessing time and minimizes the impacts of the processing delay on the vehicle following system energy efficiency. To assess the accuracy and processing time, we implemented the model on an instrumented L4-capable vehicle and conducted automated vehicle following tests in a controlled environment. In our tests, the model achieved a high level of accuracy, with a biased error of only 0.214 m and a random walk error standard deviation of 0.135 m, demonstrating its reliability across various environmental conditions and ensuring precise lane tracking. Results demonstrate reliable performance across various environmental conditions and sensor noise levels, ensuring precise lane tracking and enhanced automated maneuvering.