2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)最新文献_第3页

Network Load Adaptation for Collective Perception in V2X Communications V2X通信中集体感知的网络负载自适应

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8964988

Quentin Delooz, Andreas Festag

{"title":"Network Load Adaptation for Collective Perception in V2X Communications","authors":"Quentin Delooz, Andreas Festag","doi":"10.1109/ICCVE45908.2019.8964988","DOIUrl":"https://doi.org/10.1109/ICCVE45908.2019.8964988","url":null,"abstract":"Collective perception uses V2X communications to increase the perception capabilities of vehicles. Relying on the perceived data from their local sensors, nodes exchange information about the objects they detect in their surroundings. An object can be anything significant for the nodes' safety, e.g., obstacles on the road, other vehicles or pedestrians. The amount of data generated by each node is determined by the number of perceived objects and the generation frequency of the messages carrying the detected objects. Considering the limited bandwidth of the wireless channel, the data load generated by collective perception can easily exceed the channel capacity. In this paper, we investigate three schemes that filter the number of objects in the messages and thereby adjust the network load in order to optimize the transmission of perceived objects. Our simulation-based performance evaluation indicates that the use of filtering is an effective approach to improve network-related performance metrics, whereas the expected impairment of the perception quality is rather small. The comparison of the filtering algorithms provide insights into the tradeoff between network-related metrics and perception quality.","PeriodicalId":384049,"journal":{"name":"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127152287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Evaluation of an indoor localization system for a mobile robot 移动机器人室内定位系统的评价

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965234

V. Jiménez, C. Schwarzl, Helmut Martin

引用次数: 5

Development of a Scenario Simulation Platform to Support Autonomous Driving Verification 支持自动驾驶验证的场景仿真平台的开发

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8964914

Ch. Pilz, Gerald Steinbauer, Markus Schratter, D. Watzenig

引用次数: 11

A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling 极限操纵下自主赛车动态路径规划的软件体系结构

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965238

Johannes Betz, A. Wischnewski, Alexander Heilmeier, Felix Nobis, Leonhard Hermansdorfer, Tim Stahl, T. Herrmann, M. Lienkamp

引用次数: 25

Cloud-Based Vehicle Ride-Height Control 基于云的车辆行驶高度控制

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8964864

Konstantin Riedl, Thomas Einmüller, Andreas Noll, Andreas Allgayer, D. Reitze, M. Lienkamp

{"title":"Cloud-Based Vehicle Ride-Height Control","authors":"Konstantin Riedl, Thomas Einmüller, Andreas Noll, Andreas Allgayer, D. Reitze, M. Lienkamp","doi":"10.1109/ICCVE45908.2019.8964864","DOIUrl":"https://doi.org/10.1109/ICCVE45908.2019.8964864","url":null,"abstract":"We present a novel approach for a cloud-based ride-height control for vehicles equipped with an air suspension. The objective of this approach is to improve both efficiency and comfort, especially on single obstacles, by including vehicle-to-vehicle or vehicle-to-infrastructure (V2X) information on the road ahead in the control algorithm. The focus of this paper is the methodology of data processing on a cloud backend and includes three steps: pre-processing, clustering and allocation of streets to the clusters. In the first step, the database is reduced to obstacles relevant for driving comfort. The second step is to find clusters with a high density of obstacles on a road condition map. Finally, the probability of hitting an obstacle is calculated for each road in the area of a cluster, taking the characteristics and the topology of the road network into account. Example data is used to proof the functionality of the method. The proposed method seems to be a suitable approach for big data applications and might improve a vehicle ride-height control with regard to comfort and efficiency.","PeriodicalId":384049,"journal":{"name":"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125991248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proof of concept for Scenario-in-the-Loop (SciL) testing for autonomous vehicle technology 自动驾驶汽车技术场景在环(SciL)测试的概念验证

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965086

Z. Szalay, Mátyás Szalai, B. Tóth, T. Tettamanti, V. Tihanyi

引用次数: 11

Fault-tolerant environmental perception architecture for robust automated driving 鲁棒自动驾驶的容错环境感知体系结构

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965112

Stephanie Grubmüller, G. Stettinger, M. Sotelo, D. Watzenig

引用次数: 2

COPADRIVe - A Realistic Simulation Framework for Cooperative Autonomous Driving Applications COPADRIVe——协作式自动驾驶应用的现实仿真框架

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965161

Bruno Vieira, Ricardo Severino, E. Filho, A. Koubâa, E. Tovar

引用次数: 11

Camera Vignetting Model and its Effects on Deep Neural Networks for Object Detection 摄像机渐晕模型及其对深度神经网络目标检测的影响

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965233

Kmeid Saad, Stefan-Alexander Schneider

{"title":"Camera Vignetting Model and its Effects on Deep Neural Networks for Object Detection","authors":"Kmeid Saad, Stefan-Alexander Schneider","doi":"10.1109/ICCVE45908.2019.8965233","DOIUrl":"https://doi.org/10.1109/ICCVE45908.2019.8965233","url":null,"abstract":"In this paper, we describe a new approach for synthetic image augmentation and its advantages in training Deep Neural Networks (DNNs) for object classification and localization. To address the need for a significant amount of data when training DNNs, for image-based ADAS functions, our method relies on virtually generated scenarios augmented via a physics-based camera model. The camera model implements various optical effects on ideal-synthetic images. For the scope of this paper, we illustrate the performance differences associated with the vignetting effect when training DNNs with and without image augmentation. We show that training on images altered by our camera vignetting model yield to a better performance than using ideal-synthetic images, additionally we illustrate the relationship between the network's performance results and the implemented effect (vignetting in this case). For a start, our results open the possibility for using camera models for training neural networks on synthetic data and pave the way toward further investigations on significant optical and image sensor effects to be modeled/implemented for performance enhancement during the training process. The approach is conducted and evaluated by training a DNN for car detection using the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) and Virtual KITTI (VKITTI) datasets.","PeriodicalId":384049,"journal":{"name":"2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129815424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Accuracy Evaluation of Camera-based Vehicle Localization 基于摄像头的车辆定位精度评价

2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE) Pub Date : 2019-11-01 DOI: 10.1109/ICCVE45908.2019.8965230

Kai Cordes, Norman Nolte, N. Meine, Hellward Broszio

引用次数: 5