A Fault Tolerant Perception system for autonomous vehicles

2016 ◽  
Author(s):  
Miguel Realpe ◽  
Boris X. Vintimilla ◽  
Ljubo Vlacic
Keyword(s):  
Autonomous Vehicles ◽  
Fault Tolerant ◽  
Perception System

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 Robust Path Tracking Control for Autonomous Vehicle Based on a Novel Fault Tolerant Adaptive Model Predictive Control Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6249
Author(s):  
Keke Geng ◽  
Shuaipeng Liu
Keyword(s):  
Predictive Control ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Path Tracking ◽  
Adaptive Model ◽  
Chi Square ◽  
Model Predictive Control Algorithm ◽  
Adaptive Model Predictive Control

Autonomous vehicles are expected to completely change the development model of the transportation industry and bring great convenience to our lives. Autonomous vehicles need to constantly obtain the motion status information with on-board sensors in order to formulate reasonable motion control strategies. Therefore, abnormal sensor readings or vehicle sensor failures can cause devastating consequences and can lead to fatal vehicle accidents. Hence, research on the fault tolerant control method is critical for autonomous vehicles. In this paper, we develop a robust fault tolerant path tracking control algorithm through combining the adaptive model predictive control algorithm for lateral path tracking control, improved weight assignment method for multi-sensor data fusion and fault isolation, and novel federal Kalman filtering approach with two states chi-square detector and residual chi-square detector for detection and identification of sensor fault in autonomous vehicles. Our numerical simulation and experiment demonstrate that the developed approach can detect fault signals and identify their sources with high accuracy and sensitivity. In the double line change path tracking control experiment, when the sensors failure occurs, the proposed method shows better robustness and effectiveness than the traditional methods. It is foreseeable that this research will contribute to the development of safer and more intelligent autonomous driving system, which in turn will promote the industrial development of intelligent transportation system.

scholarly journals Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

2020 ◽  
Author(s):  
Huihui Pan ◽  
Weichao Sun ◽  
Qiming Sun ◽  
Huijun Gao
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Environmental Perception ◽  
Sensor Data ◽  
Time Data ◽  
Multiple Sensors ◽  
Perception System

Abstract Environmental perception is one of the key technologies to realize autonomous vehicles. Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system. Those sensors are very sensitive to light or background conditions, which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running. In this paper, a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed. By introducing prior features to realize the lightweight of the backbone network, the features of the input data can be extracted in real time accurately. Through the temporal and spatial correlation between sensor data, the sensor redundancy is utilized to diagnose the local and global condence of sensor data in real time, eliminate the fault data, and ensure the accuracy and reliability of data fusion. Experiments show that the network achieves the state-of-the-art results in speed and accuracy, and can accurately detect the location of the target when some sensors are out of focus or out of order.

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