Intelligent Decision-Making in Lane Detection Systems Featuring Dynamic Framework for Autonomous Vehicles

As Advanced Driver-Assistance Systems (ADAS) pave the way for autonomous vehicles, they also improve safety by decreasing the risk of hazardous events. Essential for ADAS, lane detection ensures that vehicles stay on their intended path and navigate safely. Despite their significance, the variability of lane detection algorithms can diminish performance, underscoring the need for robust and reliable solutions. We propose a novel approach to enhance decision-making in autonomous lane detection systems by introducing trust indicator metrics which help determine the usefulness of each algorithm in particular scenarios. Our dynamic framework combines a conventional algorithm with a deep learning model. This complementing combination adapts seamlessly, leveraging each other’s strengths across operational scenarios. We aim to balance the interpretability of conventional methods with the effectiveness of AI, exploring techniques like trust indicators or lane annotations. We validate the proposed framework using a self-built model vehicle demonstrator, providing insights into the real-time performance of our solution and demonstrating the potential for practical deployment.