2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)最新文献_第4页

Unsupervised Joint Multi-Task Learning of Vision Geometry Tasks 视觉几何任务的无监督联合多任务学习

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669211

P. Jha, D. Tsanev, L. Lukic

引用次数: 1

Kalman Filter Based Extended Object Tracking with a Gaussian Mixture Spatial Distribution Model 基于卡尔曼滤波的高斯混合空间分布模型扩展目标跟踪

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669221

Kolja Thormann, Shishan Yang, M. Baum

引用次数: 0

Blockchain Based Vehicle Authentication Scheme for Vehicular Ad-hoc Networks 基于区块链的车辆自组织网络认证方案

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669235

Steffie Maria Stephen, A. Jaekel

{"title":"Blockchain Based Vehicle Authentication Scheme for Vehicular Ad-hoc Networks","authors":"Steffie Maria Stephen, A. Jaekel","doi":"10.1109/ivworkshops54471.2021.9669235","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669235","url":null,"abstract":"Vehicular Ad Hoc Network (VANET) is a pervasive network, where vehicles communicate with nearby vehicles and infrastructure nodes, such as Road-side unit (RSU). Information sharing among vehicles is an essential component of an intelligent transportation system (ITS), but security and privacy concerns must be taken into consideration. Security of the network can be improved by granting access only to authenticated vehicles and restricting or revoking access for vehicles involved in misbehavior. In this paper, we present a novel blockchain based approach to authenticate vehicles and notify other vehicles about any unauthorized messages in real time. This helps protect other vehicles in the network from making critical decisions based on false or inaccurate information. In the proposed architecture, vehicles communicate with each other using pseudonyms or pseudo IDs and the Blockchain is used to securely maintain the real identity of all vehicles, which can be linked to the pseudo IDs if needed. The goal is to protect privacy or individual vehicles, while still ensuring accountability in case of misbehavior. The performance of the proposed approach is evaluated for different vehicle and attacker densities, and results demonstrate it has lower authentication delay and communication overhead compared to existing approaches.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130295988","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

ADS-B Attack Classification using Machine Learning Techniques 基于机器学习技术的ADS-B攻击分类

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669212

Thabet Kacem, Aydin Kaya, A. Keçeli, C. Catal, D. Wijesekera, P. Costa

{"title":"ADS-B Attack Classification using Machine Learning Techniques","authors":"Thabet Kacem, Aydin Kaya, A. Keçeli, C. Catal, D. Wijesekera, P. Costa","doi":"10.1109/ivworkshops54471.2021.9669212","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669212","url":null,"abstract":"Automatic Dependent Surveillance Broadcast (ADS-B) is one of the most prominent protocols in Air Traffic Control (ATC). Its key advantages derive from using GPS as a location provider, resulting in better location accuracy while offering substantially lower deployment and operational costs when compared to traditional radar technologies. ADS-B not only can enhance radar coverage but also is a standalone solution to areas without radar coverage. Despite these advantages, a wider adoption of the technology is limited due to security vulnerabilities, which are rooted in the protocol’s open broadcast of clear-text messages. In spite of the seriousness of such concerns, very few researchers attempted to propose viable approaches to address such vulnerabilities. In addition to the importance of detecting ADS-B attacks, classifying these attacks is as important since it will enable the security experts and ATC controllers to better understand the attack vector thus enhancing the future protection mechanisms. Unfortunately, there have been very little research on automatically classifying ADS-B attacks. Even the few approaches that attempted to do so considered just two classification categories, i.e. malicious message vs not malicious message. In this paper, we propose a new module to our ADS-Bsec framework capable of classifying ADS-B attacks using advanced machine learning techniques including Support Vector Machines (SVM), Decision Tree, and Random Forest (RF). Our module has the advantage that it adopts a multi-class classification approach based on the nature of the ADS-B attacks not just the traditional 2category classifiers. To illustrate and evaluate our ideas, we designed several experiments using a flight dataset from Lisbon to Paris that includes ADS-B attacks from three categories. Our experimental results demonstrated that machine learningbased models provide high performance in terms of accuracy, sensitivity, and specificity metrics.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128370543","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

Precise self-localization for last mile delivery automated driving in unstructured environments 在非结构化环境中实现最后一英里自动驾驶的精确自我定位

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669233

Paul Czerwionka, Fabian Pucks, Hans Harte, R. Blaschek, Robert Treiber, Ahmed Hussein

引用次数: 0

Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU Eagleye:基于低成本GNSS/IMU的车道级定位

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669209

Aoki Takanose, Yuki Kitsukawa, Junichi Megruo, E. Takeuchi, Alexander Carballo, K. Takeda

{"title":"Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU","authors":"Aoki Takanose, Yuki Kitsukawa, Junichi Megruo, E. Takeuchi, Alexander Carballo, K. Takeda","doi":"10.1109/ivworkshops54471.2021.9669209","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669209","url":null,"abstract":"In this paper, we propose Eagleye, an open-source software, that performs lane level localization in an urban environment. A low-cost GNSS receiver, IMU, and velocity sensor are used for position estimation. The feature of this method is that it is optimized to take full advantage of the averaging effect using time series data longer than a few tens of seconds. This optimization improves the estimation performance by reducing the GNSS multipath in urban areas. In order to verify the effectiveness of the system, we conducted accuracy evaluation of the proposed method and performance comparison tests with expensive position estimation systems. As a result of the test, we confirmed that the proposed method can estimate the relative position results with an accuracy of 0.5 m per 100m and the absolute position performance with an accuracy of 1.5 m. In addition, it was confirmed that the performance of the proposed method was equivalent to that of an expensive system. Therefore, it is considered that the proposed method can effectively estimate the location even in an urban environment.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129489690","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

Pedestrian Trajectory Prediction via Spatial Interaction Transformer Network 基于空间交互变压器网络的行人轨迹预测

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669249

Tong Su, Yu Meng, Yan Xu

引用次数: 7

Combining Semantic Self-Supervision and Self-Training for Domain Adaptation in Semantic Segmentation 结合语义自监督和自训练的语义分割领域适应

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669255

J. Niemeijer, J. P. Schäfer

{"title":"Combining Semantic Self-Supervision and Self-Training for Domain Adaptation in Semantic Segmentation","authors":"J. Niemeijer, J. P. Schäfer","doi":"10.1109/ivworkshops54471.2021.9669255","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669255","url":null,"abstract":"This work presents a two-staged, unsupervised domain adaptation process for semantic segmentation models by combining a self-training and self-supervision strategy. Self-training (i. e., training a model on self-inferred pseudo-labels) yields competitive results for domain adaptation in recent research. However, self-training depends on high-quality pseudo-labels. On the other hand, self-supervision trains the model on a surrogate task and improves its performance on the target domain without further prerequisites.Therefore, our approach improves the model’s performance on the target domain with a novel surrogate task. To that, we continuously determine class centroids of the feature representations in the network’s pre-logit layer on the source domain. Our surrogate task clusters the pre-logit feature representations on the target domain regarding these class centroids during both training stages. After the first stage, the resulting model delivers improved pseudo-labels for the additional self-training in the second stage. We evaluate our method on two different domain adaptions, a real-world domain change from Cityscapes to the Berkeley Deep Drive dataset and a synthetic to real-world domain change from GTA5 to the Cityscapes dataset. For the real-world domain change, the evaluation shows a significant improvement of the model from 46% mIoU to 54% mIoU on the target domain. For the synthetic to real-world domain change, we achieve an improvement from 38.8% to 46.42% on the real-world target domain.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127968164","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

Learning to Drive from Observations while Staying Safe 学会从观察中开车，同时保持安全

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669229

Damian Boborzi, Florian Kleinicke, Jens S. Buchner, Lars Mikelsons

引用次数: 2

Machine learning based 3D object detection for navigation in unstructured environments 基于机器学习的非结构化环境中导航3D目标检测

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) Pub Date : 2021-07-11 DOI: 10.1109/ivworkshops54471.2021.9669218

G. Nikolovski, Michael Reke, I. Elsen, S. Schiffer

{"title":"Machine learning based 3D object detection for navigation in unstructured environments","authors":"G. Nikolovski, Michael Reke, I. Elsen, S. Schiffer","doi":"10.1109/ivworkshops54471.2021.9669218","DOIUrl":"https://doi.org/10.1109/ivworkshops54471.2021.9669218","url":null,"abstract":"In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.","PeriodicalId":256905,"journal":{"name":"2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131488379","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