BoostedDim attention: A novel data-driven approach to improving LiDAR-based lane detection

Abstract

Lane detection is a fundamental component of advanced driver assistance systems, facilitating critical functionalities like Lane Keep/Change Assistance, Lane Departure Warning, Adaptive Cruise Control, and Vehicle Localization. Despite significant advancements in camera-based lane detection, it continues to confront challenges that can be effectively addressed with LiDAR technology. This research contributes to the domain of LiDAR-based lane detection across three pivotal areas. Firstly, we introduce the BoostedDim Attention method, enhancing traditional Multi-Head Self-Attention (MHA) calculations within the shallow Vision Transformers-based K-Lane baseline model. This method excels, particularly in demanding scenarios, including unknown and nighttime conditions at short ranges (0–30 m) and daytime scenarios for long ranges (30–50 m). Secondly, we devise distance-based True Positive Rate (TPR) and Lateral Error evaluation metrics, providing a more precise and tailored approach to evaluating model performance compared to conventional metrics. These metrics consider sensor-specific and task-specific factors, offering a comprehensive assessment of LiDAR-based lane detection capabilities. Lastly, our investigation sheds light on the significance of calibrated reflectivity and intensity data, revealing their impact on lane detection under various lighting conditions. Notably, we highlight the positive influence of intensity data in low-light conditions for short ranges and its adverse effect during daytime for long ranges. These findings have significant implications for enhancing autonomous driving applications and other computer vision tasks.