JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles

Saksena, Saumya Kumaar; Navaneethkrishnan, B; Mannar, Sumedh; Omkar, S. N.

doi:10.48550/arxiv.1809.01011

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Several intersection classification models have been tested using a variety of external input, such as images [1][2][3][4][5][6][7], videos [8], global positioning system (GPS) data [9], light detection and ranging [10,11], or combinations thereof [2,[12][13][14][15]. In this work, we use images as input owing to the simplicity and low cost (e.g., hardware, labor, and computation).…”

Section: Intersection Classificationmentioning

confidence: 99%

“…(2) We propose an augmentation technique for utilizing one-camera data to train a threecamera model. (3) We propose three methods of combining the information from the three cameras. (4) We perform extensive experiments using various setups to evaluate the importance of our model and justify our design choice.…”

mentioning

confidence: 99%

“…Notably, when the agent enters an intersection, it must take appropriate actions, such as turning or continuing forward. Several studies have provided methods of classifying intersections using still images or videos [1][2][3][4][5][6]; most of these studies use one-camera data. However, the one-camera approach fails to detect the intersection when the agent is inside the intersection as it can no longer detect the notable features, as illustrated in Figure 1.…”

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confidence: 99%

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Assembling three one‐camera images for three‐camera intersection classification

Astrid,

Lee

2023

ETRI Journal

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Determining whether an autonomous self‐driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three‐camera model, which would enable us to more easily compile a variety of training data to endow our model with improved generalizability. In this work, we provide three separate fusion methods (feature, early, and late) of combining the information from three cameras. Extensive pedestrian‐view intersection classification experiments show that our feature fusion model provides an area under the curve and F1‐score of 82.00 and 46.48, respectively, which considerably outperforms contemporary three‐ and one‐camera models.

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Section: Intersection Classificationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Assembling three one‐camera images for three‐camera intersection classification

Astrid,

Lee

2023

ETRI Journal

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“…These works have been evaluated and tuned in out-door environments and with a good illumination with the camera, thus providing rich data about the surrounding of the platforms, while none of the works consider the recognition of the junctions. In [15] a CNN binary classifier was proposed for outdoor road junction detection. Besides that, authors also considered the use of proposed architecture for navigation and experimentally evaluated it on commercially available MAV Bebop 2 from Parrot.…”

Section: A Related Workmentioning

confidence: 99%

Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks

Mansouri

Karvelis

Kanellakis

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aerial Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left & right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.

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“…Such an algorithm can enable simultaneous expression of generalisation ability by learning coherent representations and interpretable dynamics explanations of the world [8]. To some extent, deep neural networks, e.g., CNN, have shown generalisation ability on image classification, recognition [9] and autonomous driving/collision avoidance [10,11]…”

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confidence: 99%

Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems

Ahmedov,

Yi,

Sui

2021

Preprint

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Autonomous driving has attracted great attention from both academics and industries. To realise autonomous driving, Deep Imitation Learning (DIL) is treated as one of the most promising solutions, because it improves autonomous driving systems by automatically learning a complex mapping from human driving data, compared to manually designing the driving policy. However, existing DIL methods cannot generalise well across domains, that is, a network trained on the data of source domain gives rise to poor generalisation on the data of target domain. In the present study, we propose a novel brain-inspired deep imitation method that builds on the evidence from human brain functions, to improve the generalisation ability of deep neural networks so that autonomous driving systems can perform well in various scenarios. Specifically, humans have a strong generalisation ability which is beneficial from the structural and functional asymmetry of the two sides of the brain. Here, we design dual Neural Circuit Policy (NCP) architectures in deep neural networks based on the asymmetry of human neural networks. Experimental results demonstrate that our brain-inspired method outperforms existing methods regarding generalisation when dealing with unseen data. Our source codes and pretrained models are available at https://github.com/Intenzo21/Brain-Inspired-Deep-Imitation-Learning-for-Autonomous-Driving-Systems.Keywords Brain-inspired AI • Imitation Learning • Autonomous Vehicles Successful deep neural networks with a single, task-specific algorithm should be able to generalise well across domains. Such an algorithm can enable simultaneous expression of generalisation ability by learning coherent representations and interpretable dynamics explanations of the world [8]. To some extent, deep neural networks, e.g., CNN, have shown generalisation ability on image classification, recognition [9] and autonomous driving/collision avoidance [10,11]

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JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles

Cited by 7 publications

References 8 publications

Assembling three one‐camera images for three‐camera intersection classification

Assembling three one‐camera images for three‐camera intersection classification

Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks

Brain-Inspired Deep Imitation Learning for Autonomous Driving Systems

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