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Article
Publication date: 10 August 2015

Mahasak Ketcham and Thittaporn Ganokratanaa

The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane

Abstract

Purpose

The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane road conditions, in driving system for drivers.

Design/methodology/approach

Step 1: receiving image: the developed system is able to acquire images from video files. Step 2: splitting image: the system analyzes the splitting process of video file. Step 3: cropping image: specifying the area of interest using crop tool. Step 4: image enhancement: the system conducts the frame to convert RGB color image into grayscale image. Step 5: converting grayscale image to binary image. Step 6: segmenting and removing objects: using the opening morphological operations. Step 7: defining the analyzed area within the image using the Hough transform. Step 8: computing Houghline transform: the system operates the defined segment to analyze the Houghline transform.

Findings

This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing. The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi. The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results. The performance of the Hough transform is better than the histogram shapes.

Originality/value

This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm. The concept of this paper is to analyze between algorithms, provide a process of lane detection and search for the algorithm that has the better lane detection results.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 29 April 2022

Yingpeng Dai, Jiehao Li, Junzheng Wang, Jing Li and Xu Liu

This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the…

Abstract

Purpose

This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge.

Design/methodology/approach

A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed.

Findings

Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability.

Originality/value

This research can provide a theoretical and engineering basis for lane detection on unmanned robots.

Details

Assembly Automation, vol. 42 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 19 July 2019

Sana Bougharriou, Fayçal Hamdaoui and Abdellatif Mtibaa

This paper aims to study distance determination in vehicles, which could allow an in-car system to provide feedback and alert drivers, by either prompting the driver to take…

Abstract

Purpose

This paper aims to study distance determination in vehicles, which could allow an in-car system to provide feedback and alert drivers, by either prompting the driver to take preventative action or prepare the vehicle’s safety systems for an imminent collision. The success of a new system's deploying allows drivers to oppose the huge number of accidents and the material losses and costs associated with car accidents.

Design/methodology/approach

In this context, this paper presents estimation distance between camera and frontal vehicles based on camera calibration by combining three main steps: vanishing point extraction, lanes detection and vehicles detection in the field of 3 D real scene. This algorithm was implemented in MATLAB, and it was applied on scenes containing several vehicles in highway urban area. The method starts with the camera calibration. Then, the distance information can be calculated.

Findings

Based on experiment performance, this new method achieves robustness especially for detecting and estimating distances for multiple vehicles in a single scene. Also, this method demonstrates a higher accuracy detection rate of 0.869 in an execution time of 2.382 ms.

Originality/value

The novelty of the proposed method consists firstly on the use of an adaptive segmentation to reject the false points of interests. Secondly, the use of vanishing point has reduced the cost of using memory. Indeed, the part of the image above the vanishing point will not be processed and therefore will be deleted. The last benefit is the application of this new method on structured roads.

Details

Engineering Computations, vol. 36 no. 9
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 18 January 2016

Huajun Liu, Cailing Wang and Jingyu Yang

– This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification.

Abstract

Purpose

This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification.

Design/methodology/approach

The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient direction of linear features to separate lanes, and finally all the lanes are identified through an identification function.

Findings

The scheme and algorithms are tested on real data sets collected from an intelligent vehicle. It is more efficient and more accurate than recent similar methods for structured road, and especially multiple VPs identification and estimation of branch road can be achieved and lanes of branch road can be identified for complex scenarios based on Bayesian posterior probability verification framework. Experimental results demonstrate VPs, and lanes are practical for challenging structured and semi-structured complex road scenarios.

Originality/value

A Bayesian posterior probability verification framework is proposed to estimate multiple VPs and corresponding lanes for road scene understanding of structured or semi-structured road monocular images on intelligent vehicles.

Details

Industrial Robot: An International Journal, vol. 43 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

Book part
Publication date: 18 April 2018

Mohamed Abdel-Aty, Qi Shi, Anurag Pande and Rongjie Yu

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with…

Abstract

Purpose – This chapter provides details of research that attempts to relate traffic operational conditions on uninterrupted flow facilities (e.g., freeways and expressways) with real-time crash likelihood. Unlike incident detection, the purpose of this line of work is to proactively assess crash likelihood and potentially reduce the likelihood through proactive traffic management techniques, including variable speed limit and ramp metering among others.

Methodology – The chapter distinguishes between the traditional aggregate crash frequency-based approach to safety evaluation and the approach needed for real-time crash risk estimation. Key references from the literature are summarised in terms of the reported effect of different traffic characteristics that can be derived in near real-time, including average speed, temporal variation in speed, volume and lane-occupancy, on crash occurrence.

Findings – Traffic and weather parameters are among the real-time crash-contributing factors. Among the most significant traffic parameters is speed particularly in the form of coefficient of variation of speed.

Research implications – In the traffic safety field, traditional data sources are infrastructure-based traffic detection systems. In the future, if automatic traffic detection systems could provide reliable data at the vehicle level, new variables such as headway could be introduced. Transferability of real-time crash prediction models is also of interest. Also, the potential effects of different management strategies to reduce real-time crash risk could be evaluated in a simulation environment.

Practical implications – This line of research has been at the forefront of bringing data mining and other machine-learning techniques into the traffic management arena. We expect these analysis techniques to play a more important role in real-time traffic management, not just for safety evaluation but also for congestion pricing and alternate routing.

Details

Safe Mobility: Challenges, Methodology and Solutions
Type: Book
ISBN: 978-1-78635-223-1

Keywords

Abstract

Details

Traffic Safety and Human Behavior
Type: Book
ISBN: 978-1-78635-222-4

Open Access
Article
Publication date: 28 March 2022

Di Ao and Jialin Li

This study aims to propose a novel subjective assessment (SA) method for level 2 or level 2+ advanced driver assistance system (ADAS) with a customized case study in China.

Abstract

Purpose

This study aims to propose a novel subjective assessment (SA) method for level 2 or level 2+ advanced driver assistance system (ADAS) with a customized case study in China.

Design/methodology/approach

The proposed SA method contains six dimensions, including perception, driveability and stability, riding comfort, human–machine interaction, driver workload and trustworthiness and exceptional operating case, respectively. And each dimension subordinates several subsections, which describe the corresponding details under this dimension.

Findings

Based on the proposed SA, a case study in China is conducted. Six drivers with different driving experiences are invited to give their subjective ratings for each subsection according to a predefined rating standard. The rating results show that the ADAS from Tesla outperforms the upcoming electric vehicle in most cases.

Originality/value

The proposed SA method is beneficial for the original equipment manufacturers developing related technologies in the future.

Details

Journal of Intelligent and Connected Vehicles, vol. 5 no. 2
Type: Research Article
ISSN: 2399-9802

Keywords

Article
Publication date: 5 May 2021

Haina Song, Shengpei Zhou, Zhenting Chang, Yuejiang Su, Xiaosong Liu and Jingfeng Yang

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important…

Abstract

Purpose

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Design/methodology/approach

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

Findings

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

Originality/value

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

Details

Assembly Automation, vol. 41 no. 3
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 2 January 2018

K.M. Ibrahim Khalilullah, Shunsuke Ota, Toshiyuki Yasuda and Mitsuru Jindai

The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.

Abstract

Purpose

The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.

Design/methodology/approach

Navigation in outdoor environments is still a challenging task for an autonomous mobile robot because of the highly unstructured and different characteristics of outdoor environments. This study examines a complete vision guided real-time approach for robot navigation in urban roads based on drivable road area detection by using deep learning. During navigation, the camera takes a snapshot of the road, and the captured image is then converted into an illuminant invariant image. Subsequently, a deep belief neural network considers this image as an input. It extracts additional discriminative abstract features by using general purpose learning procedure for detection. During obstacle avoidance, the robot measures the distance from the obstacle position by using estimated parameters of the calibrated camera, and it performs navigation by avoiding obstacles.

Findings

The developed method is implemented on a wheelchair robot, and it is verified by navigating the wheelchair robot on different types of urban curve roads. Navigation in real environments indicates that the wheelchair robot can move safely from one place to another. The navigation performance of the developed method and a comparison with laser range finder (LRF)-based methods were demonstrated through experiments.

Originality/value

This study develops a cost-effective navigation method by using a single camera. Additionally, it utilizes the advantages of deep learning techniques for robust classification of the drivable road area. It performs better in terms of navigation when compared to LRF-based methods in LRF-denied environments.

Details

Industrial Robot: An International Journal, vol. 45 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

Open Access
Article
Publication date: 14 December 2018

Xiaohan Li, Wenshuo Wang, Zhang Zhang and Matthias Rötting

Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up…

1162

Abstract

Purpose

Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.

Design/methodology/approach

In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario.

Findings

It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.

Originality/value

In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

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