An efficient cooperative lane-changing algorithm for sensor- and communication-enabled automated vehicles
- Authors: Awal, Tanveer , Murshed, Manzur , Ali, Mortuza
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: A key goal in transportation system is to attain efficient road traffic through minimization of trip time, fuel consumption and pollutant-emission without compromising safety. In dense traffic lane-changes and merging are often key ingredients to cause safety hazards, traffic breakdowns and travel delays. In this paper, we propose an efficient cooperative lane-changing algorithm CLA for sensor- and communication-enabled automated vehicles to reduce the lane-changing bottlenecks. For discretionary lane-changing, we consider the advantages of the subject vehicle, the follower in the current lane and k (an integer) lag vehicles in the target lane to maximize speed gains. Our algorithm simultaneously minimizes the impact of lane-change on traffic flow and the overall trip time, fuel-consumption and pollutant-emission. For mandatory lane-changing CLA dissociates the decision-making point from the actual mandatory lane-changing point and computes a suitable lane-changing slot in order to minimize lane-changing (merging) time. Our algorithm outperforms the potential cooperative lane-changing algorithm MOBIL proposed by Kesting et al. [1] in terms of merging time and rate, waiting time, fuel consumption, average velocity and flow (especially at the point in front of the merging point) at the cost of slightly increased average trip time for the mainroad vehicles compared to MOBIL. We also highlight important directions for further research. © 2015 IEEE.
LiDAR segmentation using suitable seed points for 3D building extraction
- Authors: Abdullah, S.M. , Awrangjeb, Mohammad , Lu, Guojun
- Date: 2014
- Type: Text , Conference proceedings
- Full Text: false
- Description: Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.
Improving Naive Bayes classifier using conditional probabilities
- Authors: Taheri, Sona , Mammadov, Musa , Bagirov, Adil
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness. © 2011, Australian Computer Society, Inc.
- Description: 2003009505
On the complexity and completeness of robust biclustering algorithm (ROBA)
- Authors: Ibrahim, Yousef , Noman, Nasimul , Iba, Hitoshi
- Date: 2010
- Type: Text , Conference proceedings
- Relation: 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010; Chengdu; China; 18th- 20th June 2010 published in 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
- Full Text: false
- Reviewed:
- Description: A biclustering algorithm named ROBA has been used in a number of recent works. We present a time and space efficient implementation of ROBA that reduces the time and space complexity by an order of L where L is the number of distinct values present in the data. Our implementation runs almost 11 times faster than the existing implementation on Yeast gene expression dataset. We also improve ROBA and then use it to present an iterative algorithm that can And all perfect biclusters with constant values, constant values on rows and constant values on columns. Though our algorithm may take exponential time in the worst case, we use some subtle observations to reduce computational time and space. Experimental result reveals that our algorithm runs in reasonable time on Yeast gene expression dataset and finds almost 10 times more biclusters than ROBA. ©2010 IEEE.
- Description: 2010 4th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2010
Literature on image segmentation based on split - and - Merge techniques
- Authors: Faruquzzaman, A. B. M. , Paiker, Nafize , Arafat, Jahidul , Ali, Mortuza , Sorwar, Golam
- Date: 2008
- Type: Text , Conference proceedings , Conference paper
- Relation: ICITA 2008, Cairns, Qld., 23-26 June, ICITA, published in Proceedings of 5th International Conference on Information Technology and Application pp. 120-125.
- Full Text: false
- Reviewed:
- Description: Image segmentation is a feverish issue due to drastically increasing the use of computer and the Internet. Various algorithms have been invented on this aspect. Among them, split-and-merge (SM) algorithm is highly lucrative now-a-days due to its simplicity and effectiveness in the sector of image processing. Numerous researchers have performed their research work on this algorithm to triumph over its drawbacks for its sustainable and competent implementation. This paper has consolidated the useful consideration and proposal of various researchers to formulate a strong base of knowledge for the future researcher. It has also tinted few unsettled drawbacks of SM algorithm which will open the casement of brainstorming as well as persuade them for future research on SM algorithm, thereby allow SM algorithm to attain a globally optimal algorithm for image segmentation.
- Description: 5th International Conference on Information Technology and Applications, ICITA 2008