scholarly journals Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5120 ◽  
Author(s):  
Tao Ni ◽  
Wenhang Li ◽  
Hongyan Zhang ◽  
Haojie Yang ◽  
Zhifei Kong
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Experimental Result ◽  
Pose Prediction ◽  
Tracked Vehicle ◽  
Position Information ◽  
Cloud Data ◽  
Uneven Terrain ◽  
The Stability ◽  
Safety And Stability

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle’s pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.

scholarly journals Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3676 ◽  
Author(s):  
Tao Ni ◽  
Wenhang Li ◽  
Dingxuan Zhao ◽  
Zhifei Kong
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Active Suspension ◽  
Suspension System ◽  
Global Navigation Satellite Systems ◽  
Cloud Data ◽  
Uneven Terrain ◽  
Road Profile ◽  
Data Points ◽  
Profile Estimation

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

scholarly journals 3D Fast Object Detection Based on Discriminant Images and Dynamic Distance Threshold Clustering

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7221
Author(s):  
Baifan Chen ◽  
Hong Chen ◽  
Dian Yuan ◽  
Lingli Yu
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Long Distance ◽  
Safe Driving ◽  
Distance Threshold ◽  
Cloud Data ◽  
Perception System

The object detection algorithm based on vehicle-mounted lidar is a key component of the perception system on autonomous vehicles. It can provide high-precision and highly robust obstacle information for the safe driving of autonomous vehicles. However, most algorithms are often based on a large amount of point cloud data, which makes real-time detection difficult. To solve this problem, this paper proposes a 3D fast object detection method based on three main steps: First, the ground segmentation by discriminant image (GSDI) method is used to convert point cloud data into discriminant images for ground points segmentation, which avoids the direct computing of the point cloud data and improves the efficiency of ground points segmentation. Second, the image detector is used to generate the region of interest of the three-dimensional object, which effectively narrows the search range. Finally, the dynamic distance threshold clustering (DDTC) method is designed for different density of the point cloud data, which improves the detection effect of long-distance objects and avoids the over-segmentation phenomenon generated by the traditional algorithm. Experiments have showed that this algorithm can meet the real-time requirements of autonomous driving while maintaining high accuracy.

RPS24c Isoform Facilitates Tumor Angiogenesis Via Promoting the Stability of MVIH in Colorectal Cancer

2020 ◽  
Vol 20 (5) ◽  
pp. 388-395 ◽  
Author(s):  
Yue Wang ◽  
Youjun Wu ◽  
Kun Xiao ◽  
Yingjie Zhao ◽  
Gang Lv ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Ribosomal Protein ◽  
Real Time ◽  
Tumor Angiogenesis ◽  
Real Time Pcr ◽  
Molecular Mechanisms ◽  
Migration And Invasion ◽  
Binding Interaction ◽  
Tubule Formation ◽  
The Stability

Background: Colorectal cancer (CRC) is the second leading cause of death worldwide, and distant metastasis is responsible for the poor prognosis in patients with advanced-stage CRC. RPS24 (ribosomal protein S24) as a ribosomal protein, multiple transcript variant encoding different isoforms have been found for this gene. Our previous studies have demonstrated that RPS24 is overexpressed in CRC. However, the mechanisms underlying the role of RPS24 in tumor development have not been fully defined. Methods: Expression of RPS24 isoforms and lncRNA MVIH in CRC tissues and cell lines were quantified by real-time PCR or western blotting assay. Endothelial tube formation assay was performed to determine the effect of RPS24 on tumor angiogenesis. The cell viability of HUVEC was determined by MTT assay, and the migration and invasion ability of HUVEC were detected by transwell assay. PGK1 secretion was tested with a specific ELISA kit. Results: Here, we found that RPS24c isoform was a major contributor to tumor angiogenesis, a vital process in tumor growth and metastasis. Real-time PCR revealed that RPS24c isoform was highly expressed in CRC tissues, while other isoforms are present in both normal and CRC tissues with no statistical difference. Moreover the change of RPS24 protein level is mainly due to the fluctuation of RPS24c. Furthermore, we observed that silencing RPS24c could decrease angiogenesis by inhibiting tubule formation, HUVEC cell proliferation and migration. Additionally, we investigated the molecular mechanisms and demonstrated that RPS24c mRNA interacted with lncRNA MVIH, the binding-interaction enhanced the stability of each other, thereby activated angiogenesis by inhibiting the secretion of PGK1. Conclusion: RPS24c facilitates tumor angiogenesis via the RPS24c/MVIH/PGK1 pathway in CRC. RPS24c inhibition may be a novel option for anti-vascular treatment in CRC.

scholarly journals Underground Microseismic Event Monitoring and Localization within Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2830
Author(s):  
Sili Wang ◽  
Mark P. Panning ◽  
Steven D. Vance ◽  
Wenzhan Song
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Information Needs ◽  
Localization Algorithm ◽  
Distributed Sensors ◽  
Distributed Sensor ◽  
Microseismic Events ◽  
The Stability ◽  
Stability And Accuracy

Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

scholarly journals Optimizations for identifying reference genes in bone and cartilage bioengineering

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Xiong ◽  
Xiangyun Cheng ◽  
Chao Zhang ◽  
Roland Manfred Klar ◽  
Tao He
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Adipose Tissue ◽  
Real Time ◽  
Reference Gene ◽  
Muscle Tissue ◽  
Reference Genes ◽  
Target Genes ◽  
The Stability ◽  
And Cartilage

Abstract Background Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) remains one of the best-established techniques to assess gene expression patterns. However, appropriate reference gene(s) selection remains a critical and challenging subject in which inappropriate reference gene selction can distort results leading to false interpretations. To date, mixed opinions still exist in how to choose the most optimal reference gene sets in accodrance to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guideline. Therefore, the purpose of this study was to investigate which schemes were the most feasible for the identification of reference genes in a bone and cartilage bioengineering experimental setting. In this study, rat bone mesenchymal stem cells (rBMSCs), skeletal muscle tissue and adipose tissue were utilized, undergoing either chondrogenic or osteogenic induction, to investigate the optimal reference gene set identification scheme that would subsequently ensure stable and accurate interpretation of gene expression in bone and cartilage bioengineering. Results The stability and pairwise variance of eight candidate reference genes were analyzed using geNorm. The V0.15- vs. Vmin-based normalization scheme in rBMSCs had no significant effect on the eventual normalization of target genes. In terms of the muscle tissue, the results of the correlation of NF values between the V0.15 and Vmin schemes and the variance of target genes expression levels generated by these two schemes showed that different schemes do indeed have a significant effect on the eventual normalization of target genes. Three selection schemes were adopted in terms of the adipose tissue, including the three optimal reference genes (Opt3), V0.20 and Vmin schemes, and the analysis of NF values with eventual normalization of target genes showed that the different selection schemes also have a significant effect on the eventual normalization of target genes. Conclusions Based on these results, the proposed cut-off value of Vn/n + 1 under 0.15, according to the geNorm algorithm, should be considered with caution. For cell only experiments, at least rBMSCs, a Vn/n + 1 under 0.15 is sufficient in RT-qPCR studies. However, when using certain tissue types such as skeletal muscle and adipose tissue the minimum Vn/n + 1 should be used instead as this provides a far superior mode of generating accurate gene expression results. We thus recommended that when the stability and variation of a candidate reference genes in a specific study is unclear the minimum Vn/n + 1 should always be used as this ensures the best and most accurate gene expression value is achieved during RT-qPCR assays.

scholarly journals Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3425
Author(s):  
Huanping Li ◽  
Jian Wang ◽  
Guopeng Bai ◽  
Xiaowei Hu
Keyword(s):  
Traffic Flow ◽  
Autonomous Vehicles ◽  
Flow Model ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Transformation Model ◽  
Hydrodynamic Theory ◽  
Traffic Flow Model ◽  
Traffic System ◽  
The Stability

In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.

scholarly journals Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 657
Author(s):  
Aoki Takanose ◽  
Yoshiki Atsumi ◽  
Kanamu Takikawa ◽  
Junichi Meguro
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Estimation Method ◽  
Tall Buildings ◽  
Autonomous Driving ◽  
Reliability Estimation ◽  
Height Variation ◽  
Position Information ◽  
Real Time Kinematic ◽  
Evaluation Test

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

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