scholarly journals ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings

2021 ◽  
Vol 11 (22) ◽  
pp. 10783
Author(s):  
Felipe Franco ◽  
Max Mauro Dias Santos ◽  
Rui Tadashi Yoshino ◽  
Leopoldo Rideki Yoshioka ◽  
João Francisco Justo
Keyword(s):  
Computer Vision ◽  
Digital Cameras ◽  
Modular Architecture ◽  
Driver Assistance Systems ◽  
The Road ◽  
Lane Marking ◽  
Lane Departure ◽  
Lane Recognition ◽  
Traffic Recognition ◽  
Lane Keeping

One of the main actions of the driver is to keep the vehicle in a road lane within its markings, which could be aided with modern driver-assistance systems. Forward digital cameras in vehicles allow deploying computer vision strategies to extract the road recognition characteristics in real-time to support several features, such as lane departure warning, lane-keeping assist, and traffic recognition signals. Therefore, the road lane marking needs to be recognized through computer vision strategies providing the functionalities to decide on the vehicle’s drivability. This investigation presents a modular architecture to support algorithms and strategies for lane recognition, with three principal layers defined as pre-processing, processing, and post-processing. The lane-marking recognition is performed through statistical methods, such as buffering and RANSAC (RANdom SAmple Consensus), which selects only objects of interest to detect and recognize the lane markings. This methodology could be extended and deployed to detect and recognize any other road objects.

scholarly journals Multiple Lanes Identification for Advanced Driver Assistance System (ADAS)

2019 ◽  
Vol 8 (5S3) ◽  
pp. 533-538
Keyword(s):  
Lane Detection ◽  
Detection Methods ◽  
Learning Approaches ◽  
Drowsy Driving ◽  
The Road ◽  
Perspective Distortion ◽  
Lane Departure ◽  
Lane Recognition ◽  
Feature Based ◽  
Straight Lines

Now a days, a multi-lane recognition technique that uses the ridge features and the inverse perspective mapping (IPM) is generally used to distinguish lanes since it can evacuate the perspective distortion on lines that lie in parallel in reality. The lane detection is one of the approach to design the ADAS, if the vehicles follows the lane then there is less chance to get an accident. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. Therefore most of the researchers are attracted towards this field. But, due to the varying road conditions, it is very difficult to detect the lane. The computer vision and machine learning approaches are presents in most of the articles. In this paper, a survey of different method is presents for the road picture segmentation for the multi-lane detection. The Lane Departure Warning (LDW) system can help to reduce vehicle crashes that are caused by careless or drowsy driving. There has been much research on vision based lane detection for the LDW system. In these lane detection methods, color or edge information is utilized as a feature of the lane. The feature-based methods are usually applied to localize the lanes in the road images by extracting low-level features. On the other hand, the model-based methods use several geometrical elements to describe the lanes, including parabolic curves, hyperbola and straight lines. Feature-based methods require a dataset containing several thousand images of the roads with well-painted and prominent lane markings that are subsequently converted to features. Moreover, these methods may suffer from noise.

Computer Vision on the Road: A Lane Departure and Drowsy Driver Warning System

1995 ◽  
Author(s):  
Walter Ziegler ◽  
U. Franke ◽  
G. Renner ◽  
A. Kühnle
Keyword(s):  
Computer Vision ◽  
Warning System ◽  
The Road ◽  
Lane Departure ◽  
On The Road

Vehicle assistance for lane keeping, lane departure avoidance and yaw stability. Approach by simultaneous steering and differential braking

2010 ◽  
Vol 44 (7) ◽  
pp. 811-851
Author(s):  
Nicoleta Minoiu enache ◽  
Saïd Mammar ◽  
Sébastien Glaser ◽  
Benoit Lusetti
Keyword(s):  
Lane Departure ◽  
Yaw Stability ◽  
Lane Keeping

scholarly journals Using ADAS to Future-Proof Roads—Comparison of Fog Line Detection from an In-Vehicle Camera and Mobile Retroreflectometer

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1737
Author(s):  
Ane Dalsnes Storsæter ◽  
Kelly Pitera ◽  
Edward McCormack
Keyword(s):  
Warning System ◽  
Management Systems ◽  
Warning Systems ◽  
Lane Departure Warning ◽  
Automated Driving ◽  
Pavement Markings ◽  
Lighting Conditions ◽  
Lane Marking ◽  
Lane Departure ◽  
Pavement Marking

Pavement markings are used to convey positioning information to both humans and automated driving systems. As automated driving is increasingly being adopted to support safety, it is important to understand how successfully sensor systems can interpret these markings. In this effort, an in-vehicle lane departure warning system was compared to data collected simultaneously from an externally mounted mobile retroreflectometer. The test, performed over 200 km of driving on three different routes in variable lighting conditions and road classes found that, depending on conditions, the retroreflectometer could predict whether the car’s lane departure systems would detect markings in 92% to 98% of cases. The test demonstrated that automated driving systems can be used to monitor the state of pavement markings and can provide input on how to design and maintain road infrastructure to support automated driving features. Since data about the condition of lane marking from multiple lane departure warning systems (crowd-sourced data) can provide input into the pavement marking management systems operated by many road owners, these findings also indicate that these automated driving sensors have an important role in enhancing the maintenance of pavement markings.

scholarly journals Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 634
Author(s):  
Tarek Frahi ◽  
Francisco Chinesta ◽  
Antonio Falcó ◽  
Alberto Badias ◽  
Elias Cueto ◽  
...  
Keyword(s):  
Data Analysis ◽  
The State ◽  
Sensor Data ◽  
Topological Data Analysis ◽  
Motion Sensors ◽  
Driver Assistance Systems ◽  
The Road ◽  
Topological Features ◽  
Recent Developments ◽  
Topological Data

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

A Kalman filtering fuzzy logic algorithm for recognition of lane departure

2021 ◽  
pp. 1-8
Author(s):  
Kai Ren
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Kalman Filtering ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Fuzzy Logic Algorithm ◽  
The Road ◽  
Departure Decision ◽  
Lane Departure ◽  
Fusion Analysis

In all kinds of traffic accidents, the unconscious departure of the vehicle from the lane is one of the most important reasons leading to the occurrence of these accidents. In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.

scholarly journals Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2626
Author(s):  
Carlos Hidalgo ◽  
Ray Lattarulo ◽  
Carlos Flores ◽  
Joshué Pérez Rastelli
Keyword(s):  
Trajectory Planning ◽  
Traffic Congestion ◽  
Efficient Solutions ◽  
Test Case ◽  
Cruise Control ◽  
Automated Driving ◽  
Modular Architecture ◽  
The Road ◽  
Cooperative Adaptive Cruise Control ◽  
Multiple Vehicles

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.

Road Parameter-Based Driver Assistance System for Safe Driving

2019 ◽  
Vol 2 (4) ◽  
pp. 253-262
Author(s):  
Sai Charan Addanki ◽  
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Safe Driving ◽  
The Road ◽  
Lane Tracking ◽  
Road Type ◽  
Key Aspects ◽  
Type Classification

One of the key aspects of Advanced Driver Assistance Systems (ADAS) is ensuring the safety of the driver by maintaining a safe drivable speed. Overspeeding is one of the critical factors for accidents and vehicle rollovers, especially at road turns. This article aims to propose a driver assistance system for safe driving on Indian roads. In this regard, a camera-based classification of the road type combined with the road curvature estimation helps the driver to maintain a safe drivable speed primarily at road curves. Three Deep Convolutional Neural Network (CNN) models viz. Inception-v3, ResNet-50, and VGG-16 are being used for the task of road type classification. In this regard, the models are validated using a self-created dataset of Indian roads and an optimal performance of 83.2% correct classification is observed. For the calculation of road curvature, a lane tracking algorithm is used to estimate the curve radius of a structured road. The road type classification and the estimated road curvature values are given as inputs to a simulation-based model, CARSIM (vehicle road simulator to estimate the drivable speed). The recommended speed is then compared and analyzed with the actual speeds obtained from subjective tests.

New Lane Departure Warning System Based on Side-View Cameras

2015 ◽  
Vol 764-765 ◽  
pp. 1361-1365
Author(s):  
Cheng Yu Chiu ◽  
Chih Han Chang ◽  
Hsin Jung Lin ◽  
Tsong Liang Huang
Keyword(s):  
Warning System ◽  
Side View ◽  
Lane Departure Warning ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Warning Messages ◽  
Warning Message ◽  
Lane Departure ◽  
Assistance Systems ◽  
The Right

This paper addressed a new lane departure warning system (LDWS). We used the side-view cameras to promote Advanced Driver Assistance Systems (ADAS). A left side-view camera detected the right lane next to vehicle, and a right side-view camera detected the right lane. Two cameras processed in their algorithm and gave warning message, independently and separately. Our algorithm combined those warning messages to analyze environment situations. At the end, we used the LUXGEN MPV to test and showed results of verifications and tests.

Lane Marking Detection Based on Segments with Upper and Lower Structure

2019 ◽  
Vol 34 (02) ◽  
pp. 2055005
Author(s):  
Junjie Huang ◽  
Zhiling Wang ◽  
Huawei Liang ◽  
Linglong Lin ◽  
Biao Yu ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Urban Traffic ◽  
Middle Level ◽  
Threshold Method ◽  
Line Segments ◽  
Lane Marking ◽  
Lane Departure ◽  
Lower Structure ◽  
Multiple Disturbance ◽  
High Level

An effective and accurate lane marking detection algorithm is a fundamental element of the intelligent vehicle system and the advanced driver assistant system, which can provide important information to ensure the vehicle runs in the lane or warn the driver in case of lane departure. However, in the complex urban environment, lane markings are always affected by illumination, shadow, rut, water, other vehicles, abandoned old lane markings and non-lane markings, etc. Meanwhile, the lane markings are weak caused by hard use over time. The dash and curve lane marking detection is also a challenge. In this paper, a new lane marking detection algorithm for urban traffic is proposed. In the low-level phase, an iterative adaptive threshold method is used for image segmentation, which is especially suitable for the blurred and weakened lane markings caused by low illumination or wear. In the middle-level phase, the algorithm clusters the candidate pixels into line segments, and the upper and lower structure is used to cluster the line segments into candidate lanes, which is more suitable for curve and dashed lane markings. In the high-level phase, we compute the highest scores to get the two optimal lane markings. The optimal strategy can exclude interference similar to lane markings. We test our algorithm on Future Challenge TSD-Lane dataset and KITTI UM dataset. The results show our algorithm can effectively detect lane markings under multiple disturbance, occlusions and sharp curves.

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