Abstract
Abstract. High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.
Highlights
High Accuracy Driving Maps (HADMs) are the core component of Intelligent Driving Assistant Systems (IDAS) or Advanced Driver Assistance Systems (ADASs), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences
This paper proposes a novel method for multiple types of road features extraction from large-scale highway scene point clouds
As many road features are located on the road surfaces, we propose an algorithm that first identifies road surfaces from ground points and extracts road markings from the identified road surface data
Summary
High Accuracy Driving Maps (HADMs) are the core component of Intelligent Driving Assistant Systems (IDAS) or Advanced Driver Assistance Systems (ADASs), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. MLS systems are a promising and feasible alternative to assist in rapidly extracting highway facilities and producing HADMs. point clouds collected by MLS system contain multiple objects with a variety of shapes and sizes, complicated and incomplete structures, occlusion, varied point densities, all of which pose great challenges for automatic and robust extraction of road features. Point clouds collected by MLS system contain multiple objects with a variety of shapes and sizes, complicated and incomplete structures, occlusion, varied point densities, all of which pose great challenges for automatic and robust extraction of road features To address these challenges, extensive studies have been investigated to extract road surface features and roadside objects form MLS point clouds
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