A Review of Intelligent Self-Driving Vehicle Software Research

doi:10.3837/tiis.2019.11.002

Cited by 13 publications

(7 citation statements)

References 33 publications

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“…Yuan et al [22] proposed a method that segments roads and detects lanes based on normal maps to address difficulties in lane detection owing to the variety of noise in real driving environments, such as vehicles and shadows. Based on the study conducted by Gwak et al [23], we investigated algorithms employing various sensors used in autonomous vehicles and proposed a lane recognition model using cameras. Among the numerous vision algorithms, the Faster R-CNN Inception v2 model was developed and assessed using transfer learning [24].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles

2020

KSII TIIS

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Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed 3956 Jung et al.: Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

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Section: Related Workmentioning

confidence: 99%

Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles

2020

KSII TIIS

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“…However, LIBSVM.jl is more comprehensive than SVM.jl. LIBSVM.jl supports all libsvm models: classification c-svc, nu-svc, regression: epsilon-svr, nu-svr and distribution estimation: a class of support vector machines and ScikitLearn.jl [40] API. In addition, the model object is represented by a support vector machine of Julia type.…”

Section: Support Vector Machinementioning

confidence: 99%

“…[39] ScikitLearn.jl [40] Regression.jl [46] EmpiricalRisk.jl [47] Clustering.jl [55] QuickShiftClustering.jl [61] kpax3.jl [8] MultivariateStats.jl Backpropneuralnet.jl [52] Elm.jl [90]…”

Section: Limited Number Of Third-party Packagesmentioning

confidence: 99%

Julia Language in Machine Learning: Algorithms, Applications, and Open Issues

Gao,

Mei,

Piccialli

et al. 2020

Preprint

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Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity. The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. This paper summarizes the related research work and developments in the application of the Julia language in machine learning. It first surveys the popular machine learning algorithms that are developed in the Julia language. Then, it investigates applications of the machine learning algorithms implemented with the Julia language. Finally, it discusses the open issues and the potential future directions that arise in the use

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“…The 3D LiDAR point clouds have become one of the most significant 3D data presentations for depth information, and have been deployed in various applications, such as urban geometry mapping, autonomous driving, virtual reality, augmented reality, and more [1][2][3]. Point cloud is a set of points in a 3D metric space, which provides rich 3D information, such as geometry, color, intensity, normal, and more, to accurately measure the surrounding objects.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder–Decoder Shared MLPs with Multiple Losses

Ahyoung

Hong

2021

Remote Sensing

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Semantic segmentation of large-scale outdoor 3D LiDAR point clouds becomes essential to understand the scene environment in various applications, such as geometry mapping, autonomous driving, and more. With an advantage of being a 3D metric space, 3D LiDAR point clouds, on the other hand, pose a challenge for a deep learning approach, due to their unstructured, unorder, irregular, and large-scale characteristics. Therefore, this paper presents an encoder–decoder shared multi-layer perceptron (MLP) with multiple losses, to address an issue of this semantic segmentation. The challenge rises a trade-off between efficiency and effectiveness in performance. To balance this trade-off, we proposed common mechanisms, which is simple and yet effective, by defining a random point sampling layer, an attention-based pooling layer, and a summation of multiple losses integrated with the encoder–decoder shared MLPs method for the large-scale outdoor point clouds semantic segmentation. We conducted our experiments on the following two large-scale benchmark datasets: Toronto-3D and DALES dataset. Our experimental results achieved an overall accuracy (OA) and a mean intersection over union (mIoU) of both the Toronto-3D dataset, with 83.60% and 71.03%, and the DALES dataset, with 76.43% and 59.52%, respectively. Additionally, our proposed method performed a few numbers of parameters of the model, and faster than PointNet++ by about three times during inferencing.

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A Review of Intelligent Self-Driving Vehicle Software Research

Cited by 13 publications

References 33 publications

Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles

Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles

Julia Language in Machine Learning: Algorithms, Applications, and Open Issues

Semantic Segmentation of Large-Scale Outdoor Point Clouds by Encoder–Decoder Shared MLPs with Multiple Losses

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