5,823 research outputs found
A Flexible Modeling Approach for Robust Multi-Lane Road Estimation
A robust estimation of road course and traffic lanes is an essential part of
environment perception for next generations of Advanced Driver Assistance
Systems and development of self-driving vehicles. In this paper, a flexible
method for modeling multiple lanes in a vehicle in real time is presented.
Information about traffic lanes, derived by cameras and other environmental
sensors, that is represented as features, serves as input for an iterative
expectation-maximization method to estimate a lane model. The generic and
modular concept of the approach allows to freely choose the mathematical
functions for the geometrical description of lanes. In addition to the current
measurement data, the previously estimated result as well as additional
constraints to reflect parallelism and continuity of traffic lanes, are
considered in the optimization process. As evaluation of the lane estimation
method, its performance is showcased using cubic splines for the geometric
representation of lanes in simulated scenarios and measurements recorded using
a development vehicle. In a comparison to ground truth data, robustness and
precision of the lanes estimated up to a distance of 120 m are demonstrated. As
a part of the environmental modeling, the presented method can be utilized for
longitudinal and lateral control of autonomous vehicles
Multi-Lane Perception Using Feature Fusion Based on GraphSLAM
An extensive, precise and robust recognition and modeling of the environment
is a key factor for next generations of Advanced Driver Assistance Systems and
development of autonomous vehicles. In this paper, a real-time approach for the
perception of multiple lanes on highways is proposed. Lane markings detected by
camera systems and observations of other traffic participants provide the input
data for the algorithm. The information is accumulated and fused using
GraphSLAM and the result constitutes the basis for a multilane clothoid model.
To allow incorporation of additional information sources, input data is
processed in a generic format. Evaluation of the method is performed by
comparing real data, collected with an experimental vehicle on highways, to a
ground truth map. The results show that ego and adjacent lanes are robustly
detected with high quality up to a distance of 120 m. In comparison to serial
lane detection, an increase in the detection range of the ego lane and a
continuous perception of neighboring lanes is achieved. The method can
potentially be utilized for the longitudinal and lateral control of
self-driving vehicles
Deep Learning in Lane Marking Detection: A Survey
Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm
Recognizing Features in Mobile Laser Scanning Point Clouds Towards 3D High-definition Road Maps for Autonomous Vehicles
The sensors mounted on a driverless vehicle are not always reliable for precise localization and navigation. By comparing the real-time sensory data with a priori map, the autonomous navigation system can transform the complicated sensor perception mission into a simple map-based localization task. However, the lack of robust solutions and standards for creating such lane-level high-definition road maps is a major challenge in this emerging field.
This thesis presents a semi-automated method for extracting meaningful road features from mobile laser scanning (MLS) point clouds and creating 3D high-definition road maps for autonomous vehicles. After pre-processing steps including coordinate system transformation and non-ground point removal, a road edge detection algorithm is performed to distinguish road curbs and extract road surfaces followed by extraction of two categories of road markings. On the one hand, textual and directional road markings including arrows, symbols, and words are detected by intensity thresholding and conditional Euclidean clustering. On the other hand, lane markings (lines) are extracted by local intensity analysis and distance thresholding according to road design standards. Afterwards, centerline points in every single lane are estimated based on the position of the extracted lane markings. Ultimately, 3D road maps with precise road boundaries, road markings, and the estimated lane centerlines are created.
The experimental results demonstrate the feasibility of the proposed method, which can accurately extract most road features from the MLS point clouds. The average recall, precision, and F1-score obtained from four datasets for road marking extraction are 93.87%, 93.76%, and 93.73%, respectively. All of the estimated lane centerlines are validated using the “ground truthing” data manually digitized from the 4 cm resolution UAV orthoimages. The results of a comparison study show the better performance of the proposed method than that of some other existing methods
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