26,454 research outputs found
Sensor fusion methodology for vehicle detection
A novel sensor fusion methodology is presented, which provides intelligent vehicles with augmented environment information and knowledge, enabled by vision-based system, laser sensor and global positioning system. The presented approach achieves safer roads by data fusion techniques, especially in single-lane carriage-ways where casualties are higher than in other road classes, and focuses on the interplay between vehicle drivers and intelligent vehicles. The system is based on the reliability of laser scanner for obstacle detection, the use of camera based identification techniques and advanced tracking and data association algorithms i.e. Unscented Kalman Filter and Joint Probabilistic Data Association. The achieved results foster the implementation of the sensor fusion methodology in forthcoming Intelligent Transportation Systems
Interactive Attention Learning on Detection of Lane and Lane Marking on the Road by Monocular Camera Image
Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks. In this paper, we establish a novel multi-task encoder–decoder framework for the simultaneous detection of lanes and lane markings. This approach deploys a dual-branch architecture to extract image information from different scales. By revealing the spatial constraints between lanes and lane markings, we propose an interactive attention learning for their feature information, which involves a Deformable Feature Fusion module for feature encoding, a Cross-Context module as information decoder, a Cross-IoU loss and a Focal-style loss weighting for robust training. Without bells and whistles, our method achieves state-of-the-art results on tasks of lane marking detection (with 32.53% on IoU, 81.61% on accuracy) and lane segmentation (with 91.72% on mIoU) of the BDD100K dataset, which showcases an improvement of 6.33% on IoU, 11.11% on accuracy in lane marking detection and 0.22% on mIoU in lane detection compared to the previous methods
STEREO VISION-BASED LANE DETECTION AND TRACKING FOR INTELLIGENT VEHICLES
Lane detection is one of the key issues for intelligent vehicles. In this paper, we present a lane detection approach designed to navigate an autonomous vehicle through challenging traffic scenes based on stereo vision. In the method, both intensity and geometry cues of the road scenes are utilized and integrated for detecting and tracking the targets based on Hidden Markov Models to deal with challenging conditions and situations. It can capture both painted and physical lane boundaries. Furthermore, the geometry relationships between the stereo camera in the moving vehicle and the road are dynamically estimated and calibrated. Therefore, more accuracy and robustness can be expected in the proposed system. Experimental results in various real challenging traffic scenes show the effectiveness of the proposed system
Perception advances in outdoor vehicle detection for automatic cruise control
This paper describes a vehicle detection system based
on support vector machine (SVM) and monocular vision.
The final goal is to provide vehicle-to-vehicle time gap
for automatic cruise control (ACC) applications in the
framework of intelligent transportation systems (ITS). The
challenge is to use a single camera as input, in order to
achieve a low cost final system that meets the requirements
needed to undertake serial production in automotive industry.
The basic feature of the detected objects are first located in
the image using vision and then combined with a SVMbased classifier. An intelligent learning approach is proposed
in order to better deal with objects variability, illumination
conditions, partial occlusions and rotations. A large database
containing thousands of object examples extracted from real
road scenes has been created for learning purposes. The
classifier is trained using SVM in order to be able to classify
vehicles, including trucks. In addition, the vehicle detection
system described in this paper provides early detection of
passing cars and assigns lane to target vehicles. In the paper,
we present and discuss the results achieved up to date in real
traffic conditions.Ministerio de Educación y Cienci
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
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