17,849 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
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
We consider the paradigm of a black box AI system that makes life-critical
decisions. We propose an "arguing machines" framework that pairs the primary AI
system with a secondary one that is independently trained to perform the same
task. We show that disagreement between the two systems, without any knowledge
of underlying system design or operation, is sufficient to arbitrarily improve
the accuracy of the overall decision pipeline given human supervision over
disagreements. We demonstrate this system in two applications: (1) an
illustrative example of image classification and (2) on large-scale real-world
semi-autonomous driving data. For the first application, we apply this
framework to image classification achieving a reduction from 8.0% to 2.8% top-5
error on ImageNet. For the second application, we apply this framework to Tesla
Autopilot and demonstrate the ability to predict 90.4% of system disengagements
that were labeled by human annotators as challenging and needing human
supervision
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
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
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