2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)最新文献_第2页

Cooperative Multi-Modal Localization in Connected and Autonomous Vehicles 网联和自动驾驶汽车的协同多模态定位

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334558

Nikos Piperigkos, A. Lalos, K. Berberidis, C. Anagnostopoulos

{"title":"Cooperative Multi-Modal Localization in Connected and Autonomous Vehicles","authors":"Nikos Piperigkos, A. Lalos, K. Berberidis, C. Anagnostopoulos","doi":"10.1109/CAVS51000.2020.9334558","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334558","url":null,"abstract":"Cooperative Localization is expected to play a crucial role in various applications in the field of Connected and Autonomous vehicles (CAVs). Future 5G wireless systems are expected to enable cost-effective Vehicle-to-Everything (V2X) systems, allowing CAVs to share with the other entities of the network the data they collect and measure. Typical measurement models usually deployed for this problem, are absolute position from Global Positioning System (GPS), relative distance and azimuth angle to neighbouring vehicles, extracted from Light Detection and Ranging (LIDAR) or Radio Detection and Ranging (RADAR) sensors. In this paper, we provide a cooperative localization approach that performs multi modal-fusion between the interconnected vehicles, by representing a fleet of connected cars as an undirected graph, encoding each vehicle position relative to its neighbouring vehicles. This method is based on: i) the Laplacian Processing, a Graph Signal Processing tool that allows to capture intrinsic geometry of the undirected graph of vehicles rather than their absolute position on global coordinate system and ii) the temporal coherence due to motion patterns of the moving vehicles.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128399389","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: 10

Simulation Framework for Platooning based on Gazebo and SUMO 基于Gazebo和SUMO的队列仿真框架

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334630

Kenan Ahmic, Anel Tahirbegović, A. Tahirovic, D. Watzenig, G. Stettinger

{"title":"Simulation Framework for Platooning based on Gazebo and SUMO","authors":"Kenan Ahmic, Anel Tahirbegović, A. Tahirovic, D. Watzenig, G. Stettinger","doi":"10.1109/CAVS51000.2020.9334630","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334630","url":null,"abstract":"The role of autonomous cooperative vehicles will undoubtedly be important in Intelligent Transportation Systems (ITS) to increase both the safety and the overall efficiency of a high traffic network system. An autonomous platooning provides one promising strategy for decreasing total fuel consumption of a fleet of vehicles and potential risk of accidents, especially during long-distance transportation. In this work, we provide a proof-of-concept for a simulation framework in which it is possible to simulate platoon and other multi-vehicle systems using realistic vehicle models within different traffic scenarios, which is based on ROS, Gazebo and SUMO. The framework enables an easy-to-use perception and control modules of the autonomous driving stack for a realistic vehicle models, while preserving a convenient setup of different high traffic platooning scenarios. Consequently, it provides a platooning design step for conducting reliable development analyses and a platform for comparisons of different platooning strategies. We illustrate the effectiveness of the proposed platooning framework through three typical scenarios using a distributed model predictive control scheme with a platoon consisted of Toyota Prius car models.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123860707","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: 0

Platoon String Stability: A Passivity Perspective 排串稳定性:被动视角

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334649

C. N. Mokogwu, K. Hashtrudi-Zaad

引用次数: 2

Interaction-aware risk assessment: focus on the lateral intention 交互感知风险评估:关注横向意图

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334597

J. Villagrá, Antonio Artuñedo, Vinicius Trentin, Jorge Godoy

引用次数: 4

Welcome from the General and Technical Program Co-chairs 欢迎来自一般和技术项目的联合主席

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/cavs51000.2020.9334655

引用次数: 0

Look-ahead Horizon based Energy Optimization for Connected Hybrid Electric Vehicles 基于前瞻地平线的互联混合动力汽车能量优化

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334621

Fuguo Xu, T. Shen

引用次数: 1

Evaluation of Measurement Space Representations of Deep Multi-Modal Object Detection for Extended Object Tracking in Autonomous Driving 面向自动驾驶扩展目标跟踪的深度多模态目标检测测量空间表征评价

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334646

Lino Antoni Giefer, Razieh Khamsehashari, K. Schill

{"title":"Evaluation of Measurement Space Representations of Deep Multi-Modal Object Detection for Extended Object Tracking in Autonomous Driving","authors":"Lino Antoni Giefer, Razieh Khamsehashari, K. Schill","doi":"10.1109/CAVS51000.2020.9334646","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334646","url":null,"abstract":"The perception ability of automated systems such as autonomous cars plays an outstanding role for safe and reliable functionality. With the continuously growing accuracy of deep neural networks for object detection on one side and the investigation of appropriate space representations for object tracking on the other side both essential perception parts received special research attention within the last years. However, early fusion of multiple sensors turns the determination of suitable measurement spaces into a complex and not trivial task. In this paper, we propose the use of a deep multi-modal object detection network for the early fusion of LiDAR and camera data to serve as a measurement source for an extended object tracking algorithm on Lie groups. We develop an extended Kalman filter and model the state space as the direct product Aff(2) × ℝ6 incorporating second- and third-order dynamics. We compare the tracking performance of different measurement space representations-SO(2) × ℝ4, SO(2)2 × ℝ3 and Aff(2)-to evaluate, how our object detection network encapsulates the measurement parameters and the associated uncertainties. With our results, we show that the lowest tracking errors in the case of single object tracking are obtained by representing the measurement space by the affine group. Thus, we assume that our proposed object detection network captures the intrinsic relationships between the measurement parameters, especially between position and orientation.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127036182","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: 2

Sequence Prediction-based Proactive Caching in Vehicular Content Networks 基于序列预测的车辆内容网络主动缓存

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334683

Qiao Wang, D. Grace

{"title":"Sequence Prediction-based Proactive Caching in Vehicular Content Networks","authors":"Qiao Wang, D. Grace","doi":"10.1109/CAVS51000.2020.9334683","DOIUrl":"https://doi.org/10.1109/CAVS51000.2020.9334683","url":null,"abstract":"Proactive caching is a promising approach to achieve efficient content delivery, reduce content retrieval latency, and improve user experience in vehicular content networks. This paper proposes a mobility prediction based proactive caching scheme utilizing a sequence prediction algorithm, namely Sequence Prediction-based Proactive Caching, to predict the next possible RSU along a vehicle’s path and pre-locate relevant content. Four systems’ performance is evaluated in two areas of Las Vegas and Manchester. The obtained results in Las Vegas have shown that the proposed system outperforms the other three systems i.e., Baseline Proactive Caching system, non-proactive caching system and no-caching system. It is shown to be up to over three times and twice better than the non-proactive caching system and Baseline Proactive Caching system respectively in terms of cache performance and on average, network delay of SPPC is reduced by 18% and 24% compared with non-proactive caching system and no-caching system respectively. Performance benchmark in Manchester generalized the application of SPPC system and asserted its superiority. The paper also gives insight into solving prediction issues with data mining techniques.","PeriodicalId":409507,"journal":{"name":"2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127852966","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

A Context Aware and Traffic Adaptive Privacy Scheme in VANETs vanet中上下文感知和流量自适应的隐私方案

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334559

Ikjot Saini, Sherif Saad, A. Jaekel

引用次数: 2

Cooperative Road Geometry Estimation via Sharing Processed Camera Data

2020 IEEE 3rd Connected and Automated Vehicles Symposium (CAVS) Pub Date : 2020-11-01 DOI: 10.1109/CAVS51000.2020.9334579

A. Sakr

引用次数: 1