1,031 research outputs found
Challenges in Partially-Automated Roadway Feature Mapping Using Mobile Laser Scanning and Vehicle Trajectory Data
Connected vehicle and driver's assistance applications are greatly
facilitated by Enhanced Digital Maps (EDMs) that represent roadway features
(e.g., lane edges or centerlines, stop bars). Due to the large number of
signalized intersections and miles of roadway, manual development of EDMs on a
global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the
preferred data acquisition method to provide data for automated EDM
development. Such systems provide an MTLS trajectory and a point cloud for the
roadway environment. The challenge is to automatically convert these data into
an EDM. This article presents a new processing and feature extraction method,
experimental demonstration providing SAE-J2735 map messages for eleven example
intersections, and a discussion of the results that points out remaining
challenges and suggests directions for future research.Comment: 6 pages, 5 figure
An Empirical Evaluation of Deep Learning on Highway Driving
Numerous groups have applied a variety of deep learning techniques to
computer vision problems in highway perception scenarios. In this paper, we
presented a number of empirical evaluations of recent deep learning advances.
Computer vision, combined with deep learning, has the potential to bring about
a relatively inexpensive, robust solution to autonomous driving. To prepare
deep learning for industry uptake and practical applications, neural networks
will require large data sets that represent all possible driving environments
and scenarios. We collect a large data set of highway data and apply deep
learning and computer vision algorithms to problems such as car and lane
detection. We show how existing convolutional neural networks (CNNs) can be
used to perform lane and vehicle detection while running at frame rates
required for a real-time system. Our results lend credence to the hypothesis
that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio
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
- …