2,499 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
Dynamic User Equilibrium (DUE)
The quantitative analysis of road network traffic performed through static
assignment models yields the transport demand-supply equilibrium under
the assumption of within-day stationarity. This implies that the relevant
variables of the system (i.e. user flows, travel times, costs) are assumed to
be constant over time within the reference period. Although static
assignment models satisfactorily reproduce congestion effects on traffic flow
and cost patterns, they do not allow to represent the variation over time of
the demand flows (i.e. around the rush hour) and of the network
performances (i.e. in presence of time varying tolls, lane usage, signal plans,
link usage permission); most importantly, they cannot reproduce some
important dynamic phenomena, such as the formation and dispersion of
vehicle queues due to the temporary over-saturation of road sections, and
the spillback, that is queues propagation towards upstream roads
Optimal control of vehicle dynamics for the prevention of road departure on curved roads
Run-off-Road crashes are often associated with excessive speed in curves, which may happen when a driver is distracted or fails to compensate for reduced surface friction. This work introduces an Automated Emergency Cornering (AEC) system to protect against the major effects of over-speeding on curves, especially lateral deviation leading to lane or road departure. The AEC architecture has two levels: an upper level to perform motion planning, based on the optimal control of a nonlinear particle model, and a lower level to distribute the resulting two-dimensional acceleration reference to the available actuators. The lower level adopts the recently introduced Modified Hamiltonian Algorithm (MHA), which continuously adjusts the priority between mass-centre acceleration and yaw moment demands derived from lateral stability targets. AEC makes use of a high precision map and triggers control interventions based on vehicle kinematic states and detailed road geometry. To avoid false-positive interventions, AEC is triggered only when excessive road departure is predicted for the optimal particle motion. AEC then takes control of steering and individual wheel brake actuators to perform autonomous motion control for speed and path curvature at the limits of available friction. The AEC system is tested and evaluated
Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
Lane detection is very important for self-driving vehicles. In recent years,
computer stereo vision has been prevalently used to enhance the accuracy of the
lane detection systems. This paper mainly presents a multiple lane detection
algorithm developed based on optimised dense disparity map estimation, where
the disparity information obtained at time t_{n} is utilised to optimise the
process of disparity estimation at time t_{n+1}. This is achieved by estimating
the road model at time t_{n} and then controlling the search range for the
disparity estimation at time t_{n+1}. The lanes are then detected using our
previously published algorithm, where the vanishing point information is used
to model the lanes. The experimental results illustrate that the runtime of the
disparity estimation is reduced by around 37% and the accuracy of the lane
detection is about 99%.Comment: 5 pages, 7 figures, IEEE International Conference on Imaging Systems
and Techniques (IST) 201
Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision
Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed
Overview of Environment Perception for Intelligent Vehicles
This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The
state-of-the-art algorithms and modeling methods for intelligent
vehicles are given, with a summary of their pros and cons. A
special attention is paid to methods for lane and road detection,
traffic sign recognition, vehicle tracking, behavior analysis, and
scene understanding. In addition, we provide information about
datasets, common performance analysis, and perspectives on
future research directions in this area
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