2019 IEEE Intelligent Transportation Systems Conference (ITSC)最新文献_第8页

Deep Neural Network-Based Robust Ship Detection Under Different Weather Conditions 基于深度神经网络的不同天气条件下船舶鲁棒检测

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917475

Xin Nie, Meifang Yang, R. W. Liu

{"title":"Deep Neural Network-Based Robust Ship Detection Under Different Weather Conditions","authors":"Xin Nie, Meifang Yang, R. W. Liu","doi":"10.1109/ITSC.2019.8917475","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917475","url":null,"abstract":"Deep learning-based object detection has recently received significant attention among scholars and practitioners. However, the acquired images often suffer from visual quality degradation under severe weather conditions, which could lead to negative effects on object detection in practical applications. Most previous studies proposed to implement object detection based on the assumption that image restoration techniques (e.g., image dehazing and low-light image enhancement, etc.) could improve visual quality while boosting detection accuracy. In contrast, we assumed that the image restoration techniques (i.e., image preprocessing) may also degrade the fine image details resulting in failing to promote object detection performance. In this work, according to the physical imaging process under severe weather conditions, we directly proposed to synthetically generate the degraded images with training labels to enlarge the original training datasets, which commonly contain only clear natural images under normal weather conditions. The advanced YOLOv3 model was then trained and tested on the enlarged dataset which contain both synthetic and realistic ship images generated under different weather conditions. Experiments have been conducted to compare the proposed method with other competing methods which implement training model only with clear images and testing model with (or without) image preprocessing. Results illustrated that our model could achieve superior detection performance under different conditions.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"69 1","pages":"47-52"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81729844","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: 25

Hybrid A*-based Curvature Continuous Path Planning in Complex Dynamic Environments 复杂动态环境下基于混合A*的曲率连续路径规划

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916953

Songyi Zhang, Yu Chen, Shi-tao Chen, N. Zheng

{"title":"Hybrid A*-based Curvature Continuous Path Planning in Complex Dynamic Environments","authors":"Songyi Zhang, Yu Chen, Shi-tao Chen, N. Zheng","doi":"10.1109/ITSC.2019.8916953","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916953","url":null,"abstract":"With the progress of autonomous driving technology in recent years, motion planning has been an issue in the navigation of self-driving cars. To achieve an optimal path that meets the requirements of both smoothness and safety, vehicle kinematics and dynamics constraints should be considered. This paper proposes a novel motion planning method based on Hybrid A* for real-time and curvature-contentious path planning with local post smoothing in complex dynamic environments: (1)our method introduces parametric clothoid curves precomputed offline as basic motion primitives for rapid online planning; (2)the path obtained using our method is G2-continuous (i.e., curvature continuous) and does not have a considerable effect on the search time consumption, while also considering possible collisions and motion constraints of nonholonomic car-like vehicles; (3) the node re-expansion issue of conventional Hybrid A* is discussed and resolved by the proposed quintic spine-based local smoothing approach for complete path continuity. Hence, post smoothing and collision checking for the overall resulting path. Simulation and on-road tests have been performed to evaluate the efficiency of the proposed method. The method can be widely implemented in numerous complex scenarios.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"18 1","pages":"1468-1474"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81821917","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: 11

Attacks and Counter Defense Mechanisms for CBTC Systems: System Modeling and Availability Analysis CBTC系统的攻击与反防御机制:系统建模与可用性分析

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917082

Wenhao Wu, Bing Bu, Wei Zhang

{"title":"Attacks and Counter Defense Mechanisms for CBTC Systems: System Modeling and Availability Analysis","authors":"Wenhao Wu, Bing Bu, Wei Zhang","doi":"10.1109/ITSC.2019.8917082","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917082","url":null,"abstract":"Communication-based train control (CBTC) are automated train control systems using information communication technologies to ensure the safe operation of rail vehicles. With the development of information technology, massive commercial software, hardware products and standard communication equipment are applied to urban rail transit systems, which introduces a crowd of security threats to CBTC systems. This paper proposes a generalized stochastic Petri net model to simulate dynamic interaction between the attacker and defender to evaluate the security of CBTC systems. According to the characteristics of the system and attack-defense methods, we divide our model to the penetration phase and the disruption phase. In each phase, we provide effective means of attack and corresponding defensive measures, and the system state is determined correspondingly. The model parameters are obtained by conducting attack and defense exercises on the semi-physical simulation platform. The system transition probability is derived with the model parameter and the Nash equilibrium of the game between the attacker and defender. The system availability is obtained by calculating the steady probability of each state which can be derived from the GSPN model solution. Our analytic results reveal the seriousness of the system security situation and the significance of defensive measures for system security.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"17 1","pages":"2521-2526"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81838443","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

Performance Characteristics of Sensing-based SPS of PC5-based C-V2X Mode 4 in Crash Warning Application under Congestion 基于pc5的C-V2X模式4感知SPS在拥塞下碰撞预警应用中的性能特征

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917397

Takeshi Hirai, T. Murase

引用次数: 6

Leveraging Deep Learning Based Object Detection for Localising Autonomous Personal Mobility Devices in Sparse Maps 利用基于深度学习的目标检测在稀疏地图中定位自主个人移动设备

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917454

Maleen Jayasuriya, G. Dissanayake, Ravindra Ranasinghe, N. Gandhi

{"title":"Leveraging Deep Learning Based Object Detection for Localising Autonomous Personal Mobility Devices in Sparse Maps","authors":"Maleen Jayasuriya, G. Dissanayake, Ravindra Ranasinghe, N. Gandhi","doi":"10.1109/ITSC.2019.8917454","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917454","url":null,"abstract":"This paper presents a low cost, resource efficient localisation approach for autonomous driving in GPS denied environments. One of the most challenging aspects of traditional landmark based localisation in the context of autonomous driving, is the necessity to accurately and frequently detect landmarks. We leverage the state of the art deep learning framework, YOLO (You Only Look Once), to carry out this important perceptual task using data obtained from monocular cameras. Extracted bearing only information from the YOLO framework, and vehicle odometry, is fused using an Extended Kalman Filter (EKF) to generate an estimate of the location of the autonomous vehicle, together with it’s associated uncertainty. This approach enables us to achieve real-time sub metre localisation accuracy, using only a sparse map of an outdoor urban environment. The broader motivation of this research is to improve the safety and reliability of Personal Mobility Devices (PMDs) through autonomous technology. Thus, all the ideas presented here are demonstrated using an instrumented mobility scooter platform.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"31 1","pages":"4081-4086"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80506032","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: 3

A comparative study of the effect of Intelligent Control based Torque Vectoring Systems on EVs with different powertrain architectures 基于智能控制的转矩矢量控制系统在不同动力总成结构电动汽车上的效果对比研究

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917381

A. Parra, A. Zubizarreta, Joshué Pérez

{"title":"A comparative study of the effect of Intelligent Control based Torque Vectoring Systems on EVs with different powertrain architectures","authors":"A. Parra, A. Zubizarreta, Joshué Pérez","doi":"10.1109/ITSC.2019.8917381","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917381","url":null,"abstract":"Intelligent Transportation Systems (ITS) is currently one of the most active research areas, being electric vehicles (EVs) and their vehicle dynamics enhancement key topics. For this purpose, the development of optimal Advanced Driver-Assistance Systems (ADAS) and Advanced Vehicle Dynamics Control Systems is required. However, as electrified propulsion systems offer multiple topologies (and higher complexity), this task becomes much more difficult. In this context, the use of intelligent control techniques has been proposed as a suitable alternative to offer both performance and flexibility.In order to demonstrate the advantages of intelligent approaches and their ability to adapt to different scenarios, this work presents a comparative study of the performance of Intelligent Control based torque vectoring (TV) algorithms in electric vehicles with three different powertrain topologies: Front Wheel Driven (FWD), Rear Wheel Driven (RWD) and Four/All Wheel Driven (AWD). The same TV approach has been used for all topologies, and a skid-pad test has been selected as a critical manoeuvre for evaluating the lateral dynamics of each topology, which has been simulated using a high fidelity vehicle simulator.Results show that the same intelligent control approach can be used for different topologies without retuning its parameters, enhancing the vehicle dynamics for all cases. This demonstrates the flexibility of intelligent approaches due to their reduced model dependency. Additionally, results show that each architecture promotes a different type of dynamic behaviour in the vehicle: understeering behaviour for the FWD, oversteering behaviour for the RWD and a neutral behaviour for the AWD.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"92 1","pages":"480-485"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80310523","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

3D Map Optimization with Fully Convolutional Neural Network and Dynamic Local NDT 基于全卷积神经网络和动态局部无损检测的三维地图优化

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917130

Zebang Shen, Yichong Xu, Muchen Sun, Alexander Carballo, Qingguo Zhou

引用次数: 1

Vehicle speed prediction with RNN and attention model under multiple scenarios 基于RNN和注意力模型的多场景下车速预测

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917479

C. Shih, Pao-Wei Huang, E-Ton Yen, Pei-Kuei Tsung

引用次数: 14

Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Model 评估联网和自动驾驶汽车的环境影响:基于分类的排放模型的潜在缺陷

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917014

David Oswald, G. Scora, Nigel Williams, Peng Hao, M. Barth

{"title":"Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Model","authors":"David Oswald, G. Scora, Nigel Williams, Peng Hao, M. Barth","doi":"10.1109/ITSC.2019.8917014","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917014","url":null,"abstract":"In addition to providing safety and mobility benefits, Connected and Automated Vehicles (CAVs) have the potential to reduce fuel consumption and emissions. As new CAV applications are developed, it is valuable to estimate these potential environmental benefits, typically using vehicle activity data and emissions models. To date, most researchers in the U.S. have used the MOVES vehicle emissions model, developed and maintained by the U.S. Environmental Protection Agency (EPA). However, because MOVES uses a binning approach, it is likely underestimating the true energy and emissions savings that occur when CAV applications smooth traffic flow. To illustrate this problem, we measure and model the fuel consumption and CO2 emissions for a real-world CAV application: Eco-Approach and Departure (EAD) at signalized intersections. Real-world measurements are compared to a MOVES-based estimate, as well as to an estimate provided by the physical-based Comprehensive Modal Emissions Model (CMEM). Results show that MOVES consistently underestimates the energy and emissions benefits of the CAV application, primarily since the bin sizes in MOVES are too large to catch the nuances of traffic smoothing. On the other hand, CMEM provided a more accurate energy and emissions estimate, primarily since it uses analytical functions to model emissions and does not suffer from the same binning problem.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"1 1","pages":"3639-3644"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83388538","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: 4

Cyclist Intent Prediction using 3D LIDAR Sensors for Fully Automated Vehicles 使用3D激光雷达传感器预测全自动驾驶车辆的骑自行车者意图

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917291

Khaled Saleh, A. Abobakr, D. Nahavandi, Julie Iskander, M. Attia, Mostafa Hossny, S. Nahavandi

{"title":"Cyclist Intent Prediction using 3D LIDAR Sensors for Fully Automated Vehicles","authors":"Khaled Saleh, A. Abobakr, D. Nahavandi, Julie Iskander, M. Attia, Mostafa Hossny, S. Nahavandi","doi":"10.1109/ITSC.2019.8917291","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917291","url":null,"abstract":"One of the main barriers against the full deployment of autonomous vehicles in urban traffic environments is the understanding of the intentions and behaviours of the human around them. Moreover, understanding and predicting intentions of vulnerable road users such as cyclists is still one of the most challenging tasks. In this work, we are proposing a novel framework for the task of intent prediction of cyclists via hand signalling from point cloud scans. We utilised our developed data generation pipeline for generating synthetic point cloud scans of cyclists doing a set of hand signals in urban traffic environments. Then, we feed a sequence of the generated point cloud scans to our framework which jointly segments all cyclists instances and predicts their most probable intended actions in an end-to-end fashion. Our proposed framework has achieved superior results with 83% in F1-Measure score over the testing split of our generated dataset. Additionally, the proposed framework outperformed other compared baseline approaches with more than 39% improvement in F1-Measure score.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"12 1","pages":"2020-2026"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83398521","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: 7