scholarly journals Safe Deep Neural Network-Driven Autonomous Vehicles Using Software Safety Cages

2019 ◽  
pp. 150-160
Author(s):  
Sampo Kuutti ◽  
Richard Bowden ◽  
Harita Joshi ◽  
Robert de Temple ◽  
Saber Fallah
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Software Safety

Low-observable targets detection for autonomous vehicles based on dual-modal sensor fusion with deep learning approach

2019 ◽  
Vol 233 (9) ◽  
pp. 2270-2283
Author(s):  
Keke Geng ◽  
Wei Zou ◽  
Guodong Yin ◽  
Yang Li ◽  
Zihao Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Complex Environments ◽  
Infrared Images ◽  
Night Time ◽  
Perception System

Environment perception is a basic and necessary technology for autonomous vehicles to ensure safety and reliable driving. A lot of studies have focused on the ideal environment, while much less work has been done on the perception of low-observable targets, features of which may not be obvious in a complex environment. However, it is inevitable for autonomous vehicles to drive in environmental conditions such as rain, snow and night-time, during which the features of the targets are not obvious and detection models trained by images with significant features fail to detect low-observable target. This article mainly studies the efficient and intelligent recognition algorithm of low-observable targets in complex environments, focuses on the development of engineering method to dual-modal image (color–infrared images) low-observable target recognition and explores the applications of infrared imaging and color imaging for an intelligent perception system in autonomous vehicles. A dual-modal deep neural network is established to fuse the color and infrared images and detect low-observable targets in dual-modal images. A manually labeled color–infrared image dataset of low-observable targets is built. The deep learning neural network is trained to optimize internal parameters to make the system capable for both pedestrians and vehicle recognition in complex environments. The experimental results indicate that the dual-modal deep neural network has a better performance on the low-observable target detection and recognition in complex environments than traditional methods.

scholarly journals Anomaly Detection of CAN Bus Messages Using A Deep Neural Network for Autonomous Vehicles

2019 ◽  
Vol 9 (15) ◽  
pp. 3174 ◽  
Author(s):  
Zhou ◽  
Li ◽  
Shen
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Can Bus ◽  
Detection Accuracy ◽  
Area Network ◽  
Data Packets ◽  
Essential Components ◽  
Triplet Loss

The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.

CTL-DNNet: Effective Circular Traffic Light Recognition with a Deep Neural Network

2017 ◽  
Vol 31 (11) ◽  
pp. 1750037 ◽  
Author(s):  
Di Wang ◽  
Hong Bao ◽  
Feifei Zhang
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Road Traffic ◽  
High Accuracy ◽  
Activation Functions ◽  
Traffic Light ◽  
Traffic Lights ◽  
Learning Network ◽  
Deep Learning Network

This paper proposed an algorithm for a deep learning network for identifying circular traffic lights (CTL-DNNet). The sample labeling process uses translation to increase the number of positive samples, and the similarity is calculated to reduce the number of negative samples, thereby reducing overfitting. We use a dataset of approximately 370[Formula: see text]000 samples, with approximately 20[Formula: see text]000 positive samples and approximately 350[Formula: see text]000 negative samples. The datasets are generated from images taken at the Beijing Garden Expo. To obtain a very robust method for the detection of traffic lights, we use different layers, different cost functions and different activation functions of the depth neural network for training and comparison. Our algorithm has evaluated autonomous vehicles in varying illumination and gets the result with high accuracy and robustness. The experimental results show that CTL-DNNet is effective at recognizing road traffic lights in the Beijing Garden Expo area.

scholarly journals Pedestrian Detection Using Multispectral Images and a Deep Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2536
Author(s):  
Jason Nataprawira ◽  
Yanlei Gu ◽  
Igor Goncharenko ◽  
Shunsuke Kamijo
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Processing Time ◽  
Deep Neural Network ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
Multispectral Images ◽  
Lighting Conditions ◽  
Performance Reduction ◽  
Pedestrian Crashes

Pedestrian fatalities and injuries most likely occur in vehicle-pedestrian crashes. Meanwhile, engineers have tried to reduce the problems by developing a pedestrian detection function in Advanced Driver-Assistance Systems (ADAS) and autonomous vehicles. However, the system is still not perfect. A remaining problem in pedestrian detection is the performance reduction at nighttime, although pedestrian detection should work well regardless of lighting conditions. This study presents an evaluation of pedestrian detection performance in different lighting conditions, then proposes to adopt multispectral image and deep neural network to improve the detection accuracy. In the evaluation, different image sources including RGB, thermal, and multispectral format are compared for the performance of the pedestrian detection. In addition, the optimizations of the architecture of the deep neural network are performed to achieve high accuracy and short processing time in the pedestrian detection task. The result implies that using multispectral images is the best solution for pedestrian detection at different lighting conditions. The proposed deep neural network accomplishes a 6.9% improvement in pedestrian detection accuracy compared to the baseline method. Moreover, the optimization for processing time indicates that it is possible to reduce 22.76% processing time by only sacrificing 2% detection accuracy.

scholarly journals Car Image Classification and Recognition

2021 ◽  
Vol 9 (9) ◽  
pp. 2096-2101
Author(s):  
Dhairya Shah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Classification ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
Medical Diagnostics ◽  
Camera Location ◽  
Vehicle Positioning ◽  
Self Driving Cars

Abstract: Vehicle positioning and classification is a vital technology in intelligent transportation and self-driving cars. This paper describes the experimentation for the classification of vehicle images by artificial vision using Keras and TensorFlow to construct a deep neural network model, Python modules, as well as a machine learning algorithm. Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex, and less accurate. The outcomes are used to assess the best camera location for filming, the vehicular traffic to determine the highway occupancy. An accurate, simple, and hardware-efficient architecture is required to be developed for image classification. Keywords: Convolutional Neural Networks, Image Classification, deep neural network, Keras, Tensorflow, Python, machine learning, dataset

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