2018 IEEE Vehicular Networking Conference (VNC)最新文献

{"title":"Message Dissemination Algorithm based on Polar Grid of Transmission Range","authors":"Seho Han, Taeyoung Kim, Sukyoung Lee","doi":"10.1109/VNC.2018.8628464","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628464","url":null,"abstract":"Message dissemination in Vehicular Ad Hoc Networks (VANETs) provides many benefits to the commercial and public services. However, frequent communication disconnection or interference occurs in various network topology. In this paper, we propose efficient message dissemination algorithm. Using this algorithm, transmission nodes divide the transmission range to several grids, and select k forwarding nodes within each grid. The simulation results show that this proposed algorithm can cut unnecessary message transmission and the transmission coverage overlap.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121983887","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":"DrivAid: Augmenting Driving Analytics with Multi-Modal Information","authors":"Bozhao Qi, Peng Liu, Tao Ji, Wei Zhao, Suman Banerjee","doi":"10.1109/VNC.2018.8628415","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628415","url":null,"abstract":"The way people drive vehicles has a great impact on traffic safety, fuel consumption, and passenger experience. Many research and commercial efforts today have primarily leveraged the Inertial Measurement Unit (IMU) to characterize, profile, and understand how well people drive their vehicles. In this paper, we observe that such IMU data alone cannot always reveal a driver’s context and therefore does not provide a comprehensive understanding of a driver’s actions. We believe that an audio-visual infrastructure, with cameras and microphones, can be well leveraged to augment IMU data to reveal driver context and improve analytics. For instance, such an audio-visual system can easily discern whether a hard braking incident, as detected by an accelerometer, is the result of inattentive driving (e.g., a distracted driver) or evidence of alertness (e.g., a driver avoids a deer).The focus of this work has been to design a relatively low-cost audio-visual infrastructure through which it is practical to gather such context information from various sensors and to develop a comprehensive understanding of why a particular driver may have taken different actions. In particular, we build a system called DrivAid, that collects and analyzes visual and audio signals in real time with computer vision techniques on a vehicle-based edge computing platform, to complement the signals from traditional motion sensors. Driver privacy is preserved since the audio-visual data is mainly processed locally. We implement DrivAid on a low-cost embedded computer with GPU and high-performance deep learning inference support. In total, we have collected more than 1550 miles of driving data from multiple vehicles to build and test our system. The evaluation results show that DrivAid is able to process video streams from 4 cameras at a rate of 10 frames per second. DrivAid can achieve an average of 90% event detection accuracy and provide reasonable evaluation feedbacks to users in real time. With the efficient design, for a single trip, only around 36% of audio-visual data needs to be analyzed on average.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130473978","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":"Caching-as-a-Service in Virtualized Caches for Information-Centric Connected Vehicle Environments","authors":"Dennis Grewe, Marco Wagner, S. Schildt, M. Arumaithurai, Hannes Frey","doi":"10.1109/VNC.2018.8628354","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628354","url":null,"abstract":"Future automotive applications, such as in the domain of automated driving, will heavily rely on information retrieval in time. However, the host-centric communication model of today’s networks as well as intermittent connectivity describe big challenges due to their movement or sparse infrastructural network deployments. The loosely coupled communication model as well as the natural support of in-network caching of Information-Centric Networking (ICN) architectures are promising to overcome the challenges of future connected vehicle environments. In ICNs, mobile nodes are able to store and carry data into areas not covered by the communication network. In this paper, models are created to assess the potential of in-network caching capabilities of ICNs in connected vehicle environments using principles from point process theory. Furthermore, the concept of virtual cache areas in which nodes in an ICN can exchange cached items on demand is presented. Evaluations are made using simulations based on a real world network deployment in Austria.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133674386","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":"Poster: Hierarchical Integrity Checking in Heterogeneous Vehicular Networks","authors":"Dominik Püllen, N. Anagnostopoulos, T. Arul, S. Katzenbeisser","doi":"10.1109/VNC.2018.8628375","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628375","url":null,"abstract":"Autonomous driving is going to usher a new era of transport. The human driver will be slowly replaced by intelligent systems. The future vehicle will rely on a variety of sensors and algorithms instead of human assessment. Thus, strong requirements for security and safety have to be met, in order to guarantee the well-being of its passengers and environment. In this work, we present a privacy-friendly way of attesting both the hardware and the software components of a vehicle, in order to prove its safety and security to passengers and third parties, e.g. manufacturers and authorities. The proposed scheme introduces different hierarchical levels, in each of which an integrity identifier is calculated based on its sub-components. Finally, a single identifier is computed for the whole vehicle, in order to validate its integrity.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122047757","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":"Effect of the Configuration of Platooning Maneuvers on the Traffic Flow under Mixed Traffic Scenarios","authors":"Jesús Mena-Oreja, J. Gozálvez, M. Sepulcre","doi":"10.1109/VNC.2018.8628381","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628381","url":null,"abstract":"Automated driving and platooning are expected to augment the road capacity and improve the traffic. The authors previously demonstrated that it is necessary to take into account platooning maneuvers in order to properly understand and quantify the impact of automated driving on the traffic under mixed traffic scenarios where automated and non-automated vehicles coexist. These scenarios are particularly relevant since platooning and automated driving will be gradually introduced, and non-automated vehicles can interfere with the maneuvers. This study progresses the current state of the art by studying the impact of the configuration of platooning maneuvers on the traffic flow under mixed traffic scenarios. The study focuses on the impact of the desired and safe gaps and the maximum platoon length. These parameters determine if and how platooning maneuvers are executed. The study demonstrates that the three parameters have a significant impact on the traffic flow, and hence their configuration should be carefully studied to maximize the impact of platooning.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123435836","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":"Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage","authors":"T. Şahin, R. Khalili, Mate Boban, A. Wolisz","doi":"10.1109/VNC.2018.8628366","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628366","url":null,"abstract":"Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling.Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the-of-coverage area.Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131377895","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":"CommPact: Evaluating the Feasibility of Autonomous Vehicle Contracts","authors":"Jeremy Erickson, Shibo Chen, Melisa K. Savich, Shengtuo Hu, Z. Morley Mao","doi":"10.1109/VNC.2018.8628319","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628319","url":null,"abstract":"In Autonomous Vehicle (AV) platooning, vehicles queue up with minimal following distances for improved traffic density and fuel economy. If one vehicle is compromised and suddenly brakes, these AVs will most likely be unable to prevent a collision. In this work, we propose a proactive approach to platooning security: Autonomous Vehicle contracts, in which AVs are architected to use secure enclaves to enforce agreed-upon driving rules, such as a restriction not to brake harder than a certain threshold while the contract is in effect. We explore whether AV contracts will be feasible in worst-case emergency situations while simultaneously under attack, when it is imperative to return full autonomy to AVs as soon as possible. Through our prototype contract implementation using Intel SGX enclaves, including measurement from real-world testing of wireless On-Board Units (OBUs), we show that AV contracts can be quickly and safely terminated in the event of an emergency while retaining a false positive rate of under 0.001% per 10 hours of use. We find that individual autonomy can be returned to the vehicles of an 8-vehicle platoon under contract within 1.5 seconds of an attack, including both detection and safe vehicle separation. Smaller platoons are even quicker. Consequently, automobile manufacturers may find the additional safety offered by AV contracts to provide a net benefit.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121577807","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":"Poster: Space-Time-Polarization ICI Parallel Cancellation OFDM Systems","authors":"H. Yeh, S. Kwon, S. Doan","doi":"10.1109/VNC.2018.8628402","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628402","url":null,"abstract":"This paper presents a design for the 2 × 2 space-time-polarization (STP) inter-carrier interference (ICI) parallel cancellation (PC) orthogonal frequency division multiplexing (OFDM) system in frequency selective mobile fading channels. The use of dual-polarized antennas is a low cost- and space-effective approach (polarization diversity). In this paper, a single antenna structure employing orthogonal polarizations is proposed to replace two spatially separated uni-polarized Tx antennas and one Rx antenna. This newly designed space-time-polarization parallel cancellation (STPPC) OFDM system may achieve better simplicity and bandwidth efficiency than the conventional STPC system without expanding power or complexity.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122113735","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":"Channel Modelling for 60GHz mmWave Communication Inside Bus","authors":"Aniq Ur Rahman, Ushasi Ghosh, A. Chandra, A. Prokeš","doi":"10.1109/VNC.2018.8628315","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628315","url":null,"abstract":"The 5G vision acknowledges intravehicular communication as a means to enable passenger connectivity on the move. The capacity demand in a public transport vehicle is multi-fold compared to personal cars as there are more people on-board. In order to meet the demand, 5G standardization bodies prescribe moving the spectrum up to the millimetre wave (mmWave) regime. In this paper, we focus on buses as they are the most pervasive form of public transportation, and provide a wideband wireless channel model for 60GHz mmWave propagation inside bus. The model characterizes power delay profile (PDP) of the wireless intravehicular channel and is derived from about a thousand measured datasets within a bus. The proposed analytical model is further translated to a simple simulation algorithm which generates in-vehicle channel PDPs. The simulated PDPs are in good agreement with the measured data.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115213979","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":"A LiDAR Error Model for Cooperative Driving Simulations","authors":"Michele Segata, R. Cigno, R. Bhadani, Matt Bunting, J. Sprinkle","doi":"10.1109/VNC.2018.8628408","DOIUrl":"https://doi.org/10.1109/VNC.2018.8628408","url":null,"abstract":"Cooperative driving and vehicular network simulations have done huge steps toward high realism. They have become essential tools for performance evaluation of any kind of vehicular networking application. Yet, cooperative vehicular applications will not be built on top of wireless networking alone, but rather fusing together different data sources including sensors like radars, LiDARs, or cameras. So far, these sensors have been assumed to be ideal, i.e., without any measurement error. This paper analyzes a set of estimated distance traces obtained with a LiDAR sensor and develops a stochastic error model that can be used in cooperative driving simulations. After implementing the model within the Plexe simulation framework, we show the impact of the model on a set of cooperative driving control algorithms.","PeriodicalId":335017,"journal":{"name":"2018 IEEE Vehicular Networking Conference (VNC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129635478","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}