Object Detection and State Estimation of Autonomous Vehicles with Multi-Sensor Information Fusion

2021 ◽  
Author(s):  
Zheng Li ◽  
Yijing Wang ◽  
Zhiqiang Zuo
Keyword(s):  
Object Detection ◽  
State Estimation ◽  
Information Fusion ◽  
Autonomous Vehicles ◽  
Sensor Information

scholarly journals The Fractional Kalman Filter-Based Asynchronous Multirate Sensor Information Fusion

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Guangyue Xue ◽  
Yubin Xu ◽  
Jing Guo ◽  
Wei Zhao
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Information Fusion ◽  
The State ◽  
Fusion Method ◽  
Fusion Algorithm ◽  
Current Time ◽  
Delay Measurement ◽  
Delayed Measurement ◽  
Sensor Information

A fractional Kalman filter-based multirate sensor fusion algorithm is presented to fuse the asynchronous measurements of the multirate sensors. Based on the characteristics of multirate and delay measurement, the state is reestimated at the time when the delayed measurement occurs by using weighted fractional Kalman filter, and then the state estimation is updated at the current time when the delayed measurement arrives following the similar pattern of Kalman filter. The simulation examples are given to illustrate the effectiveness of the proposed fusion method.

Procuring cooperative intelligence in autonomous vehicles for object detection through data fusion approach

2020 ◽  
Vol 14 (11) ◽  
pp. 1410-1417 ◽  
Author(s):  
Alfred Daniel ◽  
Karthik Subburathinam ◽  
Bala Anand Muthu ◽  
Newlin Rajkumar ◽  
Seifedine Kadry ◽  
...  
Keyword(s):  
Data Fusion ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Fusion Approach

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

scholarly journals Design and Implementation of Track and Field Training Information Collection and Feedback System Based on Multi-sensor Information Fusion

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Ling Li ◽  
Chengliang Li
Keyword(s):  
Information Fusion ◽  
Feedback System ◽  
Track And Field ◽  
Information Collection ◽  
Control Group ◽  
Average Performance ◽  
Field Training ◽  
Sensor Information ◽  
Experimental Group ◽  
Field Sports

AbstractTrack and field sports are known as the "mother of sports". Whether in the field of athletics, fitness, or education, modern track and field sports have developed rapidly. The field of athletics has reached the point where it challenges the limits of humans. The development of China is inseparable from the support of science and technology, and it is inseparable from human scientific research on track and field sports. In order to improve the scientific level of track and field training methods and develop our country's sports industry, this paper designs a track and field training information collection and feedback system based on multi-sensor information fusion. In the method part, this article briefly introduces the content of track and field sports, the mode of multi-sensor information fusion and the existing sports information collection system, using weight coefficient fusion method, D-S evidence theory algorithm and Kalman filter algorithm. This paper designs an information collection and feedback system based on multi-sensor information fusion, and conducts demand analysis, comparative analysis, and data record analysis on this system. By designing the experimental group and the control group, it can be seen that the average performance of the two groups of athletes in the 50-meter run in 8 weeks has improved, and the data of the experimental group and the control group show significant differences. After the experiment, the average performance of the male athletes in the control group increased from around 8.32 to around 8.12, an increase of 4.7%. The performance of male athletes in the experimental group increased from 8.37 to 7.92, an increase of 5.6%. It can also be known that before the experiment, the average performance of the athletes in the selected control group was due to the experimental group, but after 8 weeks of experiment, the increase in the experimental group was higher than that of the control group. This shows that the data collection and feedback system using multi-sensor information fusion can be more accurately and differentiatedly applied to track and field training, and can find problems in athletes, so as to prescribe the right medicine.

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