Application of Color Filter Adjustment and K-Means Clustering Method in Lane Detection for Self-Driving Cars

Liu, Dongfang; Wang, Yaqin; Tian, Chen

doi:10.1109/irc.2019.00030

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…Unsupervised method that creates predetermined clusters from unlabeled or unclassified samples. It iteratively seeks to reduce the total sum of distances between cluster centroids and their data points by assigning each cluster a centroid [10].…”

Section: K-meansmentioning

confidence: 99%

Review on self-driving cars using neural network architectures

P¹,

Gudla²,

Telidevulapalli³

et al. 2022

World J. Adv. Res. Rev.

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A self-driving automobile is one that can sense its environment and navigate obstacles like traffic and other vehicles on its own with little to no human intervention. Although it has been debated and worked on for a very long time, Tesla was able to produce this cutting-edge technology, which is currently being used in the automotive sector. These cars started to appear in other markets in recent years as both private and public transportation (taxis etc.). In this product development, numerous businesses are involved, including Waymo, UBER, Nissan, and Nvidia. With this kind of vehicle, the efficiency, safety, and ability to reduce human error in all aspects of automobile transportation are all improved, and driving is made as safe as possible. This kind of system can revolutionize transportation for those with disabilities and support independent travel for the blind. In this article, the development of self-driving automobiles is briefly discussed. The deep learning techniques used for self-driving automobiles are thoroughly discussed in this review article. It focuses on current techniques for lane recognition, path planning, and traffic sign detection. Additionally, it covers the experimental findings related to each of the aforementioned techniques.

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Section: K-meansmentioning

confidence: 99%

Review on self-driving cars using neural network architectures

P¹,

Gudla²,

Telidevulapalli³

et al. 2022

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

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“…Although some laboratories provide remote control and tele-operation services, they are mostly oriented at educating students (Chaos et al 2013). Several other test on robotic systems are oriented on dependencies and control aspects (Liu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Intelligent automation of dental material analysis using robotic arm with Jerk optimized trajectory

Damaševičius

Maskeliūnas

Narvydas

et al. 2020

J Ambient Intell Human Comput

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Many types of biomaterial analysis require numerous repetition of the same operations. We suggest applying the principles of Total Laboratory Automation (TLA) for analysis of dental tissues in in-vitro conditions. We propose an innovative robotic platform with ABB high precision industrial robotic arm. We programmed the robot to achieve 3000 cycles of submerging for analysis of the stability and thermal wear of dental adhesive materials. We address the problem of robot trajectory planning to achieve smooth and precise trajectory while minimizing jerk. We generate different variants of trajectory using natural cubic splines and adopt the NSGA II multiobjective evolutionary algorithm to find a Pareto-optimal set of robot arm trajectories. The results demonstrate the applicability of the developed robotic platform for in-vitro experiments with dental materials. The platform is suitable for small or medium size dental laboratories.

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