31,518 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
Model-based estimation of off-highway road geometry using single-axis LADAR and inertial sensing
This paper applies some previously studied extended
Kalman filter techniques for planar road geometry estimation
to the domain of autonomous navigation of off-highway
vehicles. In this work, a clothoid model of the road geometry is
constructed and estimated recursively based on road features
extracted from single-axis LADAR range measurements. We
present a method for feature extraction of the road centerline
in the image plane, and describe its application to recursive
estimation of the road geometry. We analyze the performance of
our method against simulated motion of varied road geometries
and against closed-loop detection, tracking and following of
desert roads. Our method accomodates full 6 DOF motion of
the vehicle as it navigates, constructs consistent estimates of the
road geometry with respect to a fixed global reference frame,
and requires an estimate of the sensor pose for each range
measurement
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
Curb-intersection feature based Monte Carlo Localization on urban roads
One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicle's localization. This paper introduces a Monte Carlo Localization (MCL) method using the curb-intersection features on urban roads. We propose a novel idea of “Virtual LIDAR” to get the measurement models for these features. Under the MCL framework, above road observation is fused with odometry information, which is able to yield precise localization. We implement the system using a single tilted 2D LIDAR on our autonomous test bed and show robust performance in the presence of occlusion from other vehicles and pedestrians
Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction
Buildings are the basic carrier of social production and human life; roads
are the links that interconnect social networks. Building and road information
has important application value in the frontier fields of regional coordinated
development, disaster prevention, auto-driving, etc. Mapping buildings and
roads from very high-resolution (VHR) remote sensing images have become a hot
research topic. However, the existing methods often ignore the strong spatial
correlation between roads and buildings and extract them in isolation. To fully
utilize the complementary advantages between buildings and roads, we propose a
building-road collaborative extraction method based on multi-task and
cross-scale feature interaction to improve the accuracy of both tasks in a
complementary way. A multi-task interaction module is proposed to interact
information across tasks and preserve the unique information of each task,
which tackle the seesaw phenomenon in multitask learning. By considering the
variation in appearance and structure between buildings and roads, a
cross-scale interaction module is designed to automatically learn the optimal
reception field for different tasks. Compared with many existing methods that
train each task individually, the proposed collaborative extraction method can
utilize the complementary advantages between buildings and roads by the
proposed inter-task and inter-scale feature interactions, and automatically
select the optimal reception field for different tasks. Experiments on a wide
range of urban and rural scenarios show that the proposed algorithm can achieve
building-road extraction with outstanding performance and efficiency.Comment: 34 pages,9 figures, submitted to ISPRS Journal of Photogrammetry and
Remote Sensin
Curb-intersection feature based Monte Carlo Localization on urban roads
One of the most prominent features on an urban road is the curb, which defines the boundary of a road surface. An intersection is a junction of two or more roads, appearing where no curb exists. The combination of curb and intersection features and their idiosyncrasies carry significant information about the urban road network that can be exploited to improve a vehicle's localization. This paper introduces a Monte Carlo Localization (MCL) method using the curb-intersection features on urban roads. We propose a novel idea of “Virtual LIDAR” to get the measurement models for these features. Under the MCL framework, above road observation is fused with odometry information, which is able to yield precise localization. We implement the system using a single tilted 2D LIDAR on our autonomous test bed and show robust performance in the presence of occlusion from other vehicles and pedestrians
Adaptive Nonparametric Image Parsing
In this paper, we present an adaptive nonparametric solution to the image
parsing task, namely annotating each image pixel with its corresponding
category label. For a given test image, first, a locality-aware retrieval set
is extracted from the training data based on super-pixel matching similarities,
which are augmented with feature extraction for better differentiation of local
super-pixels. Then, the category of each super-pixel is initialized by the
majority vote of the -nearest-neighbor super-pixels in the retrieval set.
Instead of fixing as in traditional non-parametric approaches, here we
propose a novel adaptive nonparametric approach which determines the
sample-specific k for each test image. In particular, is adaptively set to
be the number of the fewest nearest super-pixels which the images in the
retrieval set can use to get the best category prediction. Finally, the initial
super-pixel labels are further refined by contextual smoothing. Extensive
experiments on challenging datasets demonstrate the superiority of the new
solution over other state-of-the-art nonparametric solutions.Comment: 11 page
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