College Station Lane Marking Datasets: Datasets for Evaluating the Effectiveness of Lane Markings for Lane Detection (04-115)[supporting dataset]

摘要: Project Description: The objective of this project is to develop a reference system for evaluating different lane markings and perception algorithms. This project validates the effectiveness of different types of lane markings for detectability on state-of-the-art lane detection (LD) algorithms. An in-depth study into the different parameters affecting the performance of LD algorithms was conducted by incorporating pavement marking material characteristics into the evaluation framework. The effect of environmental factors (Day vs Night), driving direction, lane marking material characteristics (reflective properties like Qd/RL, marking quality), lane making layouts (30ft gap vs 40ft gap, 4inch wide vs 6 inches wide), and LD evaluation characteristics (Type of LD algorithm, Near Field-of-view (FOV) vs Far FOV). Observations were made on how these different factors interact with each other and affect LD performance. 3 different annotated image datasets were also generated 1. College Station Dataset (On-road with Material data), 2. 3M panel dataset (Closed course with material data), and 3. US290 Dataset (On-road special type of markings without material data). These datasets can be used as a reference/benchmark system by researchers to evaluate their LD algorithms and how their performance relates to different types of lane markings and their material characteristics. The results obtained were presented in several conferences and poster sessions. The project also led to the publication of a technical paper and partly supported the thesis of a Ph. D. student. Data Scope: The video data in these datasets were collected by using a 5MP camera mounted …