32,034 research outputs found
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an
important role in future traffic. To avoid accidents and achieve a highly
efficient traffic flow, it is important to detect VRUs and to predict their
intentions. In this article a holistic approach for detecting intentions of
VRUs by cooperative methods is presented. The intention detection consists of
basic movement primitive prediction, e.g. standing, moving, turning, and a
forecast of the future trajectory. Vehicles equipped with sensors, data
processing systems and communication abilities, referred to as intelligent
vehicles, acquire and maintain a local model of their surrounding traffic
environment, e.g. crossing cyclists. Heterogeneous, open sets of agents
(cooperating and interacting vehicles, infrastructure, e.g. cameras and laser
scanners, and VRUs equipped with smart devices and body-worn sensors) exchange
information forming a multi-modal sensor system with the goal to reliably and
robustly detect VRUs and their intentions under consideration of real time
requirements and uncertainties. The resulting model allows to extend the
perceptual horizon of the individual agent beyond their own sensory
capabilities, enabling a longer forecast horizon. Concealments,
implausibilities and inconsistencies are resolved by the collective
intelligence of cooperating agents. Novel techniques of signal processing and
modelling in combination with analytical and learning based approaches of
pattern and activity recognition are used for detection, as well as intention
prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge
fusion, takes place on the level of perception and intention recognition. Based
on the requirements of the cooperative approach for the communication a new
strategy for an ad hoc network is proposed.Comment: 20 pages, published at Automatisiertes und vernetztes Fahren (AAET),
Braunschweig, Germany, 201
Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture
Anticipating the future actions of a human is a widely studied problem in
robotics that requires spatio-temporal reasoning. In this work we propose a
deep learning approach for anticipation in sensory-rich robotics applications.
We introduce a sensory-fusion architecture which jointly learns to anticipate
and fuse information from multiple sensory streams. Our architecture consists
of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM)
units to capture long temporal dependencies. We train our architecture in a
sequence-to-sequence prediction manner, and it explicitly learns to predict the
future given only a partial temporal context. We further introduce a novel loss
layer for anticipation which prevents over-fitting and encourages early
anticipation. We use our architecture to anticipate driving maneuvers several
seconds before they happen on a natural driving data set of 1180 miles. The
context for maneuver anticipation comes from multiple sensors installed on the
vehicle. Our approach shows significant improvement over the state-of-the-art
in maneuver anticipation by increasing the precision from 77.4% to 90.5% and
recall from 71.2% to 87.4%.Comment: Follow-up of ICCV 2015 Brain4Cars http://www.brain4cars.co
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
In this work we propose a vehicular sensor-rich platform and learning
algorithms for maneuver anticipation. For this purpose we equip a car with
cameras, Global Positioning System (GPS), and a computing device to capture the
driving context from both inside and outside of the car. In order to anticipate
maneuvers, we propose a sensory-fusion deep learning architecture which jointly
learns to anticipate and fuse multiple sensory streams. Our architecture
consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory
(LSTM) units to capture long temporal dependencies. We propose a novel training
procedure which allows the network to predict the future given only a partial
temporal context. We introduce a diverse data set with 1180 miles of natural
freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds
before they occur in real-time with a precision and recall of 90.5\% and 87.4\%
respectively.Comment: Journal Version (ICCV and ICRA combination with more system details)
http://brain4cars.co
2D Car Detection in Radar Data with PointNets
For many automated driving functions, a highly accurate perception of the
vehicle environment is a crucial prerequisite. Modern high-resolution radar
sensors generate multiple radar targets per object, which makes these sensors
particularly suitable for the 2D object detection task. This work presents an
approach to detect 2D objects solely depending on sparse radar data using
PointNets. In literature, only methods are presented so far which perform
either object classification or bounding box estimation for objects. In
contrast, this method facilitates a classification together with a bounding box
estimation of objects using a single radar sensor. To this end, PointNets are
adjusted for radar data performing 2D object classification with segmentation,
and 2D bounding box regression in order to estimate an amodal 2D bounding box.
The algorithm is evaluated using an automatically created dataset which consist
of various realistic driving maneuvers. The results show the great potential of
object detection in high-resolution radar data using PointNets
Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a
wide range of innovative applications that can fundamentally change the way
cyber-physical systems (CPSs) are designed. CPSs are a modern generation of
systems with synergic cooperation between computational and physical potentials
that can interact with humans through several new mechanisms. The main
advantages of using UAVs in CPS application is their exceptional features,
including their mobility, dynamism, effortless deployment, adaptive altitude,
agility, adjustability, and effective appraisal of real-world functions anytime
and anywhere. Furthermore, from the technology perspective, UAVs are predicted
to be a vital element of the development of advanced CPSs. Therefore, in this
survey, we aim to pinpoint the most fundamental and important design challenges
of multi-UAV systems for CPS applications. We highlight key and versatile
aspects that span the coverage and tracking of targets and infrastructure
objects, energy-efficient navigation, and image analysis using machine learning
for fine-grained CPS applications. Key prototypes and testbeds are also
investigated to show how these practical technologies can facilitate CPS
applications. We present and propose state-of-the-art algorithms to address
design challenges with both quantitative and qualitative methods and map these
challenges with important CPS applications to draw insightful conclusions on
the challenges of each application. Finally, we summarize potential new
directions and ideas that could shape future research in these areas
SafeDrive: Enhancing Lane Appearance for Autonomous and Assisted Driving Under Limited Visibility
Autonomous detection of lane markers improves road safety, and purely visual
tracking is desirable for widespread vehicle compatibility and reducing sensor
intrusion, cost, and energy consumption. However, visual approaches are often
ineffective because of a number of factors; e.g., occlusion, poor weather
conditions, and paint wear-off. We present an approach to enhance lane marker
appearance for assisted and autonomous driving, particularly under poor
visibility. Our method, named SafeDrive, attempts to improve visual lane
detection approaches in drastically degraded visual conditions. SafeDrive finds
lane markers in alternate imagery of the road at the vehicle's location and
reconstructs a sparse 3D model of the surroundings. By estimating the geometric
relationship between this 3D model and the current view, the lane markers are
projected onto the visual scene; any lane detection algorithm can be
subsequently used to detect lanes in the resulting image. SafeDrive does not
require additional sensors other than vision and location data. We demonstrate
the effectiveness of our approach on a number of test cases obtained from
actual driving data recorded in urban settings.Comment: arXiv admin note: text overlap with arXiv:1701.0844
MAT-Fly: an educational platform for simulating Unmanned Aerial Vehicles aimed to detect and track moving objects
The main motivation of this work is to propose a simulation approach for a
specific task within the UAV (Unmanned Aerial Vehicle) field, i.e., the visual
detection and tracking of arbitrary moving objects. In particular, it is
described MAT-Fly, a numerical simulation platform for multi-rotors aircraft
characterized by the ease of use and control development. The platform is based
on Matlab and the MathWorks Virtual Reality (VR) and Computer Vision System
(CVS) toolboxes that work together to simulate the behavior of a drone in a 3D
environment while tracking a car that moves a long a non trivial path. The VR
toolbox has been chosen due to the familiarity that students have with Matlab
and because it allows to move the attention to the classifier, the tracker, the
reference generator and the trajectory tracking control thanks to its simple
structure. The overall architecture is quite modular so that each block can be
easily replaced with others by simplifying the development phase and by
allowing to add even more functionalities.
The simulation platform makes easy and quick to insert and to remove flight
control system components, testing and comparing different plans when computer
vision algorithms are in the loop. In an automatic way, the proposed simulator
is able to acquire frames from the virtual scenario, to search for one or more
objects on which it has been trained during the learning phase, and to track
the target position applying a trajectory control addressing what is well-known
in the literature as an image-based visual servoing problem.
Some simple testbeds have been presented in order to show the effectiveness
and robustness of the proposed approach as well as the platform works. We
released the software as open-source, making it available for educational
purposes
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
High quality perception is essential for autonomous driving (AD) systems. To
reach the accuracy and robustness that are required by such systems, several
types of sensors must be combined. Currently, mostly cameras and laser scanners
(lidar) are deployed to build a representation of the world around the vehicle.
While radar sensors have been used for a long time in the automotive industry,
they are still under-used for AD despite their appealing characteristics
(notably, their ability to measure the relative speed of obstacles and to
operate even in adverse weather conditions). To a large extent, this situation
is due to the relative lack of automotive datasets with real radar signals that
are both raw and annotated. In this work, we introduce CARRADA, a dataset of
synchronized camera and radar recordings with range-angle-Doppler annotations.
We also present a semi-automatic annotation approach, which was used to
annotate the dataset, and a radar semantic segmentation baseline, which we
evaluate on several metrics. Both our code and dataset are available online.Comment: 8 pages, 5 figues. Accepted at ICPR 2020. Erratum: results in Table
III have been updated since the ICPR proceedings, models are selected using
the PP metric instead of the previously used PR metri
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Lane detection in driving scenes is an important module for autonomous
vehicles and advanced driver assistance systems. In recent years, many
sophisticated lane detection methods have been proposed. However, most methods
focus on detecting the lane from one single image, and often lead to
unsatisfactory performance in handling some extremely-bad situations such as
heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In
fact, lanes are continuous line structures on the road. Consequently, the lane
that cannot be accurately detected in one current frame may potentially be
inferred out by incorporating information of previous frames. To this end, we
investigate lane detection by using multiple frames of a continuous driving
scene, and propose a hybrid deep architecture by combining the convolutional
neural network (CNN) and the recurrent neural network (RNN). Specifically,
information of each frame is abstracted by a CNN block, and the CNN features of
multiple continuous frames, holding the property of time-series, are then fed
into the RNN block for feature learning and lane prediction. Extensive
experiments on two large-scale datasets demonstrate that, the proposed method
outperforms the competing methods in lane detection, especially in handling
difficult situations.Comment: IEEE Transactions on Vehicular Technology, 69(1), 202
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