A Convolutional Neural Network-Based End-to-End Self-Driving Using LiDAR and Camera Fusion: Analysis Perspectives in a Real-World Environment

Park, Mingyu; Kim, Hyeonseok; Park, Seongkeun

doi:10.3390/electronics10212608

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…A detailed comparison of the proposed method's performance with state-of-the-art methods is mentioned in Table 5. By Seeing 5 its is clearly evident that the classification performance of the proposed method is better than the method discussed in [17], [21], [12] [15], [23], [20], and [13] while the performance is comparable with the methods discussed in [22] and [28].…”

Section: Comparison With Existing Methods On Livdet 2015 Databasementioning

confidence: 96%

An open patch generator based fingerprint presentation attack detection using generative adversarial network

Rai,

Anshul,

Jha

et al. 2023

Multimed Tools Appl

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Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of applications in areas including national borders and commercial applications. Presentation attacks can be performed by fabricating the fake fingerprint of a user with or without the intention of the subject. This paper presents a dynamic ensemble of deep learning and handcrafted features to detect presentation attacks in known-material and unknown-material protocols. The proposed model is a dynamic ensemble of deep CNN and handcrafted features empowered deep neural networks both of which learn their parameters together. The proposed presentation attack detection model, in this way, utilizes the capabilities of both classification techniques and exhibits better performance than their individual results. The proposed model's performance is validated using benchmark LivDet 2015, 2017, and 2019 databases, with an overall accuracy of 96.10%, 96.49%, and 95.99% attained on them, respectively. The proposed model outperforms state-of-the-art methods in benchmark protocols of presentation attack detection in terms of classification accuracy.

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Section: Comparison With Existing Methods On Livdet 2015 Databasementioning

confidence: 96%

An open patch generator based fingerprint presentation attack detection using generative adversarial network

Rai,

Anshul,

Jha

et al. 2023

Multimed Tools Appl

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“…Experimental results indicate that the proposed affordable, compact, and robust fusion system outperforms benchmark models and can be efficiently used in real-time for the vehicle's environment perception [58] CNN-based real-time semantic segmentation of 3D lidar data for autonomous vehicle perception based on the projection method and the adaptive break point detector method Practical implementation and satisfactory speed and accuracy of the proposed method [59] E2E self-driving algorithm using a CNN that predicts the vehicles' longitudinal and lateral control values based on the input camera images and 2D lidar point cloud data…”

Section: Reference Description Of Application Conclusionmentioning

confidence: 97%

Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector

Lopac

Jurdana

Brnelić

et al. 2022

Sensors

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The development of light detection and ranging (lidar) technology began in the 1960s, following the invention of the laser, which represents the central component of this system, integrating laser scanning with an inertial measurement unit (IMU) and Global Positioning System (GPS). Lidar technology is spreading to many different areas of application, from those in autonomous vehicles for road detection and object recognition, to those in the maritime sector, including object detection for autonomous navigation, monitoring ocean ecosystems, mapping coastal areas, and other diverse applications. This paper presents lidar system technology and reviews its application in the modern road transportation and maritime sector. Some of the better-known lidar systems for practical applications, on which current commercial models are based, are presented, and their advantages and disadvantages are described and analyzed. Moreover, current challenges and future trends of application are discussed. This paper also provides a systematic review of recent scientific research on the application of lidar system technology and the corresponding computational algorithms for data analysis, mainly focusing on deep learning algorithms, in the modern road transportation and maritime sector, based on an extensive analysis of the available scientific literature.

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“…Meanwhile, Ref. [ 13 ] proposed predicting longitudinal and lateral control values using LIDAR and camera fusion, relying on CNN architectures based on Inception [ 14 ] and ResNet [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction

Itu,

Danescu

2023

Sensors

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Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming increasingly popular for driver assistance, with precise predictions of vehicle speed and emergency braking being important for road safety enhancement, especially in the prevention of speed-related accidents. In this research paper, we introduce the implementation of a convolutional neural network (CNN) model tailored for the prediction of vehicle velocity, braking events, and emergency braking, employing sequential image sequences and velocity data as inputs. The CNN model is trained on a dataset featuring sequences of 20 consecutive images and corresponding velocity values, all obtained from a moving vehicle navigating through road-traffic scenarios. The model’s primary objective is to predict the current vehicle speed, braking actions, and the occurrence of an emergency-brake situation using the information encoded in the preceding 20 frames. We subject our proposed model to an evaluation on a dataset using regression and classification metrics, and comparative analysis with existing published work based on recurrent neural networks (RNNs). Through our efforts to improve the prediction accuracy for velocity, braking behavior, and emergency-brake events, we make a substantial contribution to improving road safety and offer valuable insights for the development of perception-based techniques in the field of autonomous vehicles.

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A Convolutional Neural Network-Based End-to-End Self-Driving Using LiDAR and Camera Fusion: Analysis Perspectives in a Real-World Environment

Cited by 11 publications

References 21 publications

An open patch generator based fingerprint presentation attack detection using generative adversarial network

An open patch generator based fingerprint presentation attack detection using generative adversarial network

Application of Laser Systems for Detection and Ranging in the Modern Road Transportation and Maritime Sector

Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction

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