Obstacle classification and detection for vision based navigation for autonomous driving

Deepika, N.; Variyar, V. V. Sajith

doi:10.1109/icacci.2017.8126154

Cited by 25 publications

(8 citation statements)

References 3 publications

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“…Although on-road obstacle detection and classification is one of the key tasks for self-driving vehicles, we noticed that the performance of studies is not high for the Indian road scenario [36,[91][92][93]. Another important task for autonomous car is collision prediction [24,94,95,110].…”

Section: Suggestions On Deep Learning (Derin öğRenme öNerileri)mentioning

confidence: 97%

“…A fully convolutional network is proposed to predict pixelwise semantic labeling of on-road unexpected obstacles such as lost cargo by Ramos et al [24]. Deepika and Variyar [92] used the SegNet encoderdecoder architecture for pixel-wise semantic segmentation of the video frame followed by an obstacle detection algorithm. Dairi et al [93] designed a hybrid encoder that integrates deep Boltzmann machines (DBM) and auto-encoders (AE) for obstacle detection.…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

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Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Yapıcı¹,

Tekerek²,

Topaloğlu³

2019

GMBD

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Derin öğrenme (Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma, konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır. Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında bilgiler vererek araştırmacılara rehberlik etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat çekerek olası araştırma konularını listeliyoruz. Günümüzde, Derin öğrenme yöntemleri nesne tanıma alanında insanlardan daha iyi sonuçlara ulaşmıştır. DL üzerine yapılan çalışmalar dikkate alındığında, bu başarının otonom araçlar, tıbbi görüntü işleme, büyük veri analizi ve karakter tanıma gibi alanlarda da gerçekleşeceği öngörülmektedir. https://dx.Deep learning (DL) is an important machine learning field that has achieved considerable success in many research areas. In the last decade, the-state-of-the-art studies on many research areas such as computer vision, object recognition, speech recognition, and natural language processing were especially led to the awakening of the artificial intelligence from deep sleep. Nowadays, many researchers try to find solutions to many problems in various fields under the light of DL methods. In this study, it is presented important knowledge to guide about DL models and challenging topics that can be used in DL for researchers. This study investigated DL studies which are made in the most popular and challenging fields such as autonomous vehicles, natural language processing, handwritten character recognition, signature verification, voice and video recognition, medical image processing, and big data. Furthermore, this study points out the remaining challenges of these research areas that can be solved by DL, and discusses future topics to help the researchers. In the present day, Deep learning methods have reached better results than humans in object recognition. According to the literature studies on DL, It is foreseen that this success will be achieved in areas such as autonomous vehicles, medical image processing, big data analysis, and character recognition. https://dx.

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Section: Suggestions On Deep Learning (Derin öğRenme öNerileri)mentioning

confidence: 97%

Section: Autonomous Vehiclesmentioning

confidence: 99%

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Yapıcı¹,

Tekerek²,

Topaloğlu³

2019

GMBD

View full text Add to dashboard Cite

show abstract

“…Semantic segmentation aims to categorize every pixel in the gathered data [ 29 ]. Deep Learning and Convolutional Neural Networks have proven to be successful in this area [ 30 , 31 ]. The next steps are detection and classification of the particular segments (objects) of the environment.…”

Section: Introductionmentioning

confidence: 99%

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

Sobczak

Filus

Domański

et al. 2021

Sensors

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With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a key component of autonomous driving systems, SLAM, frameworks targeting off-road and safety-critical environments. It also includes taking into consideration the non-idealistic nature of the real-life sensors, associated phenomena and measurement errors. We created a LiDAR simulator that delivers accurate 3D point clouds in real time. The point clouds are generated based on the sensor placement and the LiDAR type that can be set using configurable parameters. We evaluate our solution based on comparison of the results using an actual device, Velodyne VLP-16, on real-life tracks and the corresponding simulations. We measure the error values obtained using Google Cartographer SLAM algorithm and the distance between the simulated and real point clouds to verify their accuracy. The results show that our simulation (which incorporates measurement errors and the rolling shutter effect) produces data that can successfully imitate the real-life point clouds. Due to dedicated mechanisms, it is compatible with the Robotic Operating System (ROS) and can be used interchangeably with data from actual sensors, which enables easy testing, SLAM algorithm parameter tuning and deployment.

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“…Because of this necessity, in recent years, machine learning techniques, especially classification algorithms, commenced to be used in transportation sector as an interdisciplinary approach. The underlying goals for these solutions are to predict traffic flow [1], classify vehicle images [2], identify different transportation modes [3], analyse traffic incident's severity [4], mitigate unfavourable environmental impacts (i.e., to optimize energy usage [5]), develop autonomous driving system [6], and improve the productivity and efficiency of transportation systems.…”

Section: Introductionmentioning

confidence: 99%

“…It is clear that there is an order among those values and that we can write large > medium > small. Although there are many classification studies [2][3][4][5][6] performed in transportation sector, to the best of our knowledge, there has been no prior detailed investigation for ordinal classification in transportation sector. Considering this drawback, the study presented in this article focuses on the application of ordinal classification algorithms on realworld transportation datasets.…”

Section: Introductionmentioning

confidence: 99%

EBOC: Ensemble-Based Ordinal Classification in Transportation

Yıldırım

Birant

2019

Journal of Advanced Transportation

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Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EBOC) approach which suggests bagging and boosting (AdaBoost algorithm) methods as a solution for ordinal classification problem in transportation sector. This article also compares the proposed EBOC approach with ordinal class classifier and traditional tree-based classification algorithms (i.e., C4.5 decision tree, RandomTree, and REPTree) in terms of accuracy. The results indicate that the proposed EBOC approach achieves better classification performance than the conventional solutions.

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Obstacle classification and detection for vision based navigation for autonomous driving

Cited by 25 publications

References 3 publications

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation

EBOC: Ensemble-Based Ordinal Classification in Transportation

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