14,916 research outputs found
Improved LaneNet for Lane Detection
Lane detection is a critical component in autonomous vehicles and advanced driver assistance systems (ADAS), enabling accurate lane tracking and vehicle positioning. While traditional lane detection methods based on handcrafted features and heuristics have limitations in challenging environments, the adoption of machine learning (ML) techniques has shown promise. However, many existing ML models struggle with detecting a variable number of lanes, making them less effective in complex driving scenarios.
A simple solution involves the use of High Definition (HD) Maps. HD Maps offer comprehensive road information necessary for autonomous driving, but their high cost and inflexibility pose challenges for frequent updates and modifications. This research proposes an innovative approach, the Improved LaneNet (ILaneNet) network, to strike a balance between ML techniques and HD maps. By augmenting input images with a lane parameter namely the number of lanes, we aim to enhance lane detection accuracy without incurring the prohibitive costs of HD maps. ILaneNet seeks to achieve real-time precision in locating and tracking lane markings, even in challenging conditions like inadequate lighting and intricate road layouts.
The objective of this study is to develop a flexible, cost-effective, and robust lane detection system that adapts to diverse driving scenarios. By incorporating pertinent information into the network, we demonstrate improved adaptability and potential advancements in autonomous driving technologies. We also introduce new evaluation metrics namely capacity, lost capacity and unsafe driving measure to assess lane detection techniques more comprehensively. We also propose evaluation of lane detection techniques by using a lane abstraction approach instead of the traditional line abstraction method. Through extensive evaluation and comparisons, we showcase the superiority of ILaneNet over LaneNet in detecting lanes. This research contributes to bridging the gap between ML techniques and HD maps, offering a viable solution for effective and efficient lane detection in autonomous vehicles and ADAS
Characterizing driving behavior using automatic visual analysis
In this work, we present the problem of rash driving detection algorithm
using a single wide angle camera sensor, particularly useful in the Indian
context. To our knowledge this rash driving problem has not been addressed
using Image processing techniques (existing works use other sensors such as
accelerometer). Car Image processing literature, though rich and mature, does
not address the rash driving problem. In this work-in-progress paper, we
present the need to address this problem, our approach and our future plans to
build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fĂĽl alatt kell rákeresni a "Stereo Vision" cĂmre Ă©s az 1. fejezetre
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
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 anticipate driving maneuvers a few seconds before they occur.
For this purpose we equip a car with cameras and a computing device to capture
the driving context from both inside and outside of the car. We propose an
Autoregressive Input-Output HMM to model the contextual information alongwith
the maneuvers. We evaluate our approach on 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 with over 80\% F1-score in real-time.Comment: ICCV 2015, http://brain4cars.co
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
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
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