2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)最新文献

{"title":"Sybil Detection in Connected Vehicle Systems via Angle-of-Arrival Estimation","authors":"Tianye Ma, Yidan Hu, A. Aseeri, Mark M. Nejad, Rui Zhang","doi":"10.1109/MOST57249.2023.00029","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00029","url":null,"abstract":"The emerging Intelligent Transportation Systems (ITS) and the proliferation of Connected Vehicles (CVs) are widely expected to greatly improve road safety, traffic efficiency, and driving comfort. At the same time, the vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications that ITS and CVs rely on also introduce new security challenges. Sybil attack is one of the most serious security threats to CV-based ITS, in which a Sybil attacker creates and operates multiple fake CVs from a single physical CV to inject and disseminate false information to mislead the ITS into making suboptimal decisions, e.g., causing fake traffic jams. This paper proposes a novel physical measurement-based method to detect Sybil attacks and identify Sybil CVs. We observe that it is impossible for a single malicious physical CV to be presented at multiple claimed positions at the same time. Second, the Angle of Arrival (AoA) measurement depends on the physical locations of the transmitter and the receiver, which is difficult to forge in practice. Based on these observations, our scheme takes advantage of the inconsistency between their claimed positions and measured AoAs for Sybil attack detection. Detailed simulation studies using both synthetic and real vehicular mobility traces confirm that the proposed scheme can detect Sybil attacks and differentiate Sybil CVs from legitimate CVs with high accuracy.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122979239","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}

{"title":"SLAM Sharing Among Heterogeneous Sensors","authors":"Ren Zhong, Liangkai Liu, Weisong Shi","doi":"10.1109/MOST57249.2023.00025","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00025","url":null,"abstract":"The advancement of Simultaneously Localization and Mapping (SLAM) has enabled robots to accurately locate themselves in unknown environments with sensors such as LiDARs and Cameras while building a corresponding map. Re-using this map later can ensure accurate and robust localization if the environment does not change significantly. Current SLAM studies mainly focus on improving the performance of SLAM algorithm to gain better localization accuracy. However, the discrepancies between localization sensor and mapping sensor such as accuracy and resolution, may impact the localization in the shared map. The impact factors of map sharing performance has not been widely investigated. Understanding the impact factors can facilitate the implementation of map sharing system to extend the usage of SLAM map. In this paper, we utilize two representative SLAM systems, NDT SLAM and ORB SLAM, to study the potential impact factors of using a shared map for heterogeneous sensors. Specifically, we evaluate the impact of three key factors (map, localization algorithm and sensor) on map sharing performance. With three LiDARs and three cameras, we record a dataset and build two groups of maps of the same environment. By applying these maps for localization, we derive four insights into the relation between localization performance and variability of the critical factors. Specifically, we find that Lidar-based SLAM performs stable to the discrepancy of Lidar sensors. In contrast, visual-based SLAM is sensitive to the shared map’s quality and the camera’s focal length.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115771903","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}

{"title":"USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education","authors":"Yunpeng Jack Zhang, Kailai Wang, Lingguang Song, A. Lendasse, Houbing Song, Zhu Han, Rakesh M. Verma, Arlei Silva, Carlos E. Rubio-Medrano, Zhixia Li, Guohui Zhang","doi":"10.1109/MOST57249.2023.00036","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00036","url":null,"abstract":"The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of “Reducing Transportation Cybersecurity Risks.” CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128282742","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}

{"title":"Sniffer Faster R-CNN: A Joint Camera-LiDAR Object Detection Framework with Proposal Refinement","authors":"Sudip Dhakal, Qi Chen, Deyuan Qu, D. Carillo, Qing Yang, Song Fu","doi":"10.1109/MOST57249.2023.00009","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00009","url":null,"abstract":"In this paper we present Sniffer Faster R-CNN (SFR-CNN), a novel camera-LiDAR sensor fusion framework for fast and accurate object detection in autonomous driving scenarios. The proposed detection framework architecture uses both LiDAR point clouds and Camera RGB images to generate region proposals. Current implementation of the regional proposal network (RPN) requires the generation of a large number of region proposals, majority of which are unproductive. As such, we devise a novel proposal refinement algorithm, to jointly optimize and filter a number of proposals in RPN through the combined application of both sets of LiDAR and image-based proposals thereby accelerating the LiDAR-Camera fusion algorithm without sacrificing detection precision and accuracy. Our experiments show that number of proposals is a complementary factor in determining the computational overhead in a detection network. Our proposed architecture is shown to produce state of art results on the KITTI joint object detection benchmark with the comparison being based on the execution time. While maintaining efficient detection accuracy we decrease the computational overhead by more than 20 % on the KITTI dataset.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122873465","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}

{"title":"Extending Automotive Vision System by Unmanned Aerial Vehicles","authors":"Demetrius Johnson, Olivia Pellegrini, Ryan Sauer, Jonathan Schall, Khairul Mottakin, Zheng Song","doi":"10.1109/MOST57249.2023.00031","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00031","url":null,"abstract":"Unmanned aerial vehicles (UAVs) provide bird’s-eye view video, which can significantly extend the vision systems of ground vehicles. However, processing the video on the drone itself may drain the drone’s battery rapidly, while sending the video to the ground vehicle for analysis may suffer from higher delay. In this poster, we introduce our ongoing project idea about processing the time-sensitive regions of the video on the drone and transmitting the processed results along with the time-insensitive regions to the car for decision-making and further analysis. To evaluate our idea, we build an experimental system using toy cars and programmable drones. We implement an image-based object detection application which can be deployed on our experimental drone-car system. As a future work direction, we will test with different workload distributions between the drone and the car and report the resulting latency and energy consumption.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124631617","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}

{"title":"Presenting a Statistical Approach for Transforming Standardized German Traffic Surveys into Origin-Destination Matrices","authors":"Jens Klinker, Joseph Yu, Mariana Avezum-Mercer, Stephan Jonas","doi":"10.1109/MOST57249.2023.00026","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00026","url":null,"abstract":"This paper presents a method for generating Origin-Destination Matrices (ODMs) for the city of Munich using traffic count data from a Germany-wide study conducted by the German Federal Ministry of Transport and Digital Infrastructure (MiD-Study). The results show that the data provided by the MiD-Study was correctly translated into an ODM, thereby providing an interpretable demand format for traffic simulations. Due to the consistent design of the MiD-Study, the approach is also applied to Hamburg and is extensible to 18 further cities and one city-state (Bremen) covered in the MiD-Study. The produced ODMs for Munich and Hamburg are accessible for researchers at: https://nextcloud.in.tum.de/index.php/s/gT48xDzT88YJGQK","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122878718","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}

{"title":"FLOW: A Scalable Multi-Model Federated Learning Framework on the Wheels","authors":"Yongtao Yao, N. Ammar, Weisong Shi","doi":"10.1109/MOST57249.2023.00010","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00010","url":null,"abstract":"The highly mobility nature of connected vehicles poses significant challenges in the research area of federated learning, and to the best of our knowledge, the existing federated learning approaches do not consider the problem of training multi-model for constantly on-the-wheel moving vehicles. To bridge this gap, we design and implement FLOW, a scalable multi-model federated learning framework for highly mobile connected vehicles, which includes three essential components: (1) a dynamic client vehicle selection algorithm to deal with problems such as signal loss or weak signals, which may prevent some vehicles from participating in the training cluster; (2) a well-designed model allocation algorithm to select appropriate vehicle computing units for specific model training tasks; (3) geofencing not independent and identically distributed (non-i.i.d) data training, which can make models more robust and generalizable to different geographic driving area. Finally, we compare the proposed framework with centralized training and explore the performance of four aggregation protocols. The experiment results demonstrated the effectiveness of FLOW for the real-world applications.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130203713","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}

{"title":"Effects of Neural Network Architecture on Topography Estimation From Satellite Imagery for Multi-Terrain Autonomous Vehicle Path Planning and Control","authors":"Ryan Lynch, Sumedh Beknalkar, Jack Lynch, A. Mazzoleni, M. Bryant","doi":"10.1109/MOST57249.2023.00021","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00021","url":null,"abstract":"Global warming is one of the world’s most pressing issues. The study of its effects on the polar ice caps and other arctic environments, however, can be hindered by the often dangerous and difficult to navigate terrain found there. Multi-terrain autonomous vehicles can assist researchers by providing a mobile platform on which to collect data in these harsh environments while avoiding any risk to human life and speeding up the research process. The mechanical design and ultimate efficacy of these autonomous robotic vehicles depends largely on the specific missions they are deployed for, but terrain conditions can vary wildly geographically as well as seasonally, making mission planning for these unmanned vehicles more difficult. This paper proposes the use of various UNet-based neural network architectures to generate digital elevation maps from satellite images, and explores and compares their efficacy on a single set of training and validation datasets generated from satellite imagery. These digital elevation maps generated by the model could be used by researchers not only to track the change in arctic topography over time, but to quickly provide autonomous exploratory research rovers with the topographical information necessary to decide on optimal paths during the mission. This paper analyzes different model architectures and training schemes: a traditional UNet, a traditional UNet with data augmentation, a UNet with a single active skip-layer vision transformer (ViT), and a UNet with multiple active skip-layer ViT. Each model was trained on a dataset of satellite images and corresponding digital elevation maps of Ellesmere Island, Canada. Utilizing ViTs did not demonstrate a significant improvement in UNet performance, though this could change with longer training. This paper proposes opportunities to improve performance for these neural networks, as well as next steps for further research, including improving the diversity of images in the dataset, generating a testing dataset from a completely different geographic location, and allowing the models more time to train.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131610106","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}

{"title":"Improving GPS-Based Mode of Transport Detection in Multi-Modal Trips using Stop Analysis","authors":"Jens Klinker, Mariana Avezum-Mercer, Stephan Jonas","doi":"10.1109/MOST57249.2023.00023","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00023","url":null,"abstract":"This paper presents an extension to existing GPS-based approaches for tracking modes of transportation in multimodal trips. The extension focuses on analyzing stops and mapping them to surrounding public transport stations in order to improve the accuracy of the mode of transport detection. The proposed method is evaluated using data from the city of Munich, resulting in a 17% improvement of the F1-Score, from 73% to 90%. It is applicable to any GPS-based mode of transport detection system to potentially improve their accuracy.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132943272","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}

{"title":"Integration of Formal Specification and Traffic Simulation for Scenario-Based Validation","authors":"Quentin Goss, M. Akbaş","doi":"10.1109/MOST57249.2023.00030","DOIUrl":"https://doi.org/10.1109/MOST57249.2023.00030","url":null,"abstract":"Scenario-based testing has been used for validation and verification of autonomous vehicles in various settings. In these efforts, simulation platforms are extensively utilized to run and analyze test scenarios, which is crucial for the validation of perception, decision-making and action in different traffic situations. Providing formal specification for scenarios used in these simulations is also critical for standardization, repeatability and verification. In this paper, we propose an approach which integrates an open-source network-based traffic simulator with an open-source formal scenario description language designed for formal scenario specification of autonomous vehicle validation scenarios. This approach provides the capability to describe, generate, and share abstract traffic scenarios for validation. It also allows integration with higher fidelity or physical testing methods as it utilizes a widely used scenario specification methodology. We provide an integration of formal specification and traffic simulation and also show the potential of such an integration through simulation examples.","PeriodicalId":338621,"journal":{"name":"2023 IEEE International Conference on Mobility, Operations, Services and Technologies (MOST)","volume":"91 24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116305244","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}