2019 IEEE Intelligent Transportation Systems Conference (ITSC)最新文献_第10页

Attention-based Gated Recurrent Unit for Links Traffic Speed Forecasting 基于注意力的通道交通速度预测门控循环单元

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917027

G. Khodabandelou, Mehdi Katranji, Sami Kraiem, W. Kheriji, F. Hadj-Selem

{"title":"Attention-based Gated Recurrent Unit for Links Traffic Speed Forecasting","authors":"G. Khodabandelou, Mehdi Katranji, Sami Kraiem, W. Kheriji, F. Hadj-Selem","doi":"10.1109/ITSC.2019.8917027","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917027","url":null,"abstract":"With urge of demands on efficient transport planning policies along with surge of travel flow volumes due to fast urbanization, traffic speed forecasting becomes a canonical and thriving research domain. Furthermore, the vehicles speed plays a critical role in the level of congestion. Traffic speed estimation then helps transport authorities as well as network users to handle congestion over road infrastructures or at least provides a global picture of daily passenger flow. In this work, we propose the first methodology to forecast the future traffic speed over the road segments (i.e. links) exclusively based on traffic flow data using floating car data. For this study, we pre-process over one million vehicles flow for several network links spread all over the Greater Paris. A attention-based recurrent neural network is used to capture the correlation between the temporal sequences of traffic flow and that of speed. The attention layer learns patterns from weights of near-term traffic flow, thus extracts the inherent interdependency of traffic speed to many factors (e.g. incidents, rush hour, land use, etc.) in non-free-flow conditions. The results demonstrate the efficiency of the proposed model in traffic speed forecasting excluding additional data such as historic traffic speed and network graph contrary to cutting-edge work in the field. This is a substantial property since it allows avoiding the cumbersomeness in data mixing and facilitating resource availability.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"16 1","pages":"2577-2583"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79146427","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

Human-like Highway Trajectory Modeling based on Inverse Reinforcement Learning 基于逆强化学习的仿人公路轨迹建模

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916970

Ruoyu Sun, Shaochi Hu, Huijing Zhao, M. Moze, F. Aioun, F. Guillemard

引用次数: 4

Extraction and Analysis of Risk Factors from Chinese Railway Accident Reports 中国铁路事故报告中的风险因素提取与分析

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917094

L. Hua, Wei Zheng, Shigen Gao

引用次数: 9

Optimized sensor placement for dependable roadside infrastructures 为可靠的路边基础设施优化传感器位置

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917197

Florian Geissler, Ralf Graefe

引用次数: 11

Runtime Verification of Communications-based Train Control with Parametric Hybrid Automata 基于通信的参数混合自动机列车控制运行时验证

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917282

Ming Chai, Haifeng Wang, Hongjie Liu, J. Lv, Qian Hu

{"title":"Runtime Verification of Communications-based Train Control with Parametric Hybrid Automata","authors":"Ming Chai, Haifeng Wang, Hongjie Liu, J. Lv, Qian Hu","doi":"10.1109/ITSC.2019.8917282","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917282","url":null,"abstract":"The communications-based train control (CBTC) is a typical safety-critical system that protects and directs train operations in urban rail transit. It is suggested to provide on-going safety protections for the automatic train protection, which is a kernel function of the CBTC. Runtime verification is a technique for monitoring system executions against safety requirements. A particular challenge in implementing of a runtime verification system for the CBTC is the appropriate monitor specification. This paper presents a novel dynamic monitoring generation method to the problem. The train control procedures of the CBTC is formalized by parametric hybrid automata (PHA), which introduces notations of parametric expressions for flow, transition conditions and invariants. With an observation, the PHA is instantiated to a standard hybrid automaton. The monitor specification is then generated automatically by calculating the reachable set of the automaton with respect to some selected safety-related properties. The presented method is evaluated in a hard-ware in the loop CBTC platform, which is developed with realistic engineering data of Beijing Yizhuang metro line. The experiment results show that the approach is feasible, and various dangerous of the CBTC system are prevented from developing into accidents of train collisions.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"101 ","pages":"2160-2165"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91465208","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

Hybrid State Estimation Combining Artificial Neural Network and Physical Model 结合人工神经网络和物理模型的混合状态估计

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916954

P. Sieberg, S. Blume, N. Harnack, Niko Maas, D. Schramm

{"title":"Hybrid State Estimation Combining Artificial Neural Network and Physical Model","authors":"P. Sieberg, S. Blume, N. Harnack, Niko Maas, D. Schramm","doi":"10.1109/ITSC.2019.8916954","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916954","url":null,"abstract":"This article presents a hybrid state estimation using vehicle dynamics as an application. The knowledge about the dynamic states are essential in the vehicle. Ultimately, the built-in control algorithms are using these states to exploit safety, comfort, and performance. In most cases, the states of the vehicle are measured directly. Nevertheless, direct measurement is not profitable or difficult to implement for all states of vehicle dynamics. In this case, state estimators are used. In the past, classical approaches such as modelling of the physical systems have been used for estimation. Due to the continuous developments in the field of computing hardware, methods of machine learning can now also be used in this context. The presented article includes artificial neural networks. With this method, a transfer behavior can be mapped without having knowledge about the system to be estimated. A major problem of such artificial neural networks, however, is the traceability as well as checking the robustness for universal use. Therefore, the artificial neural network is coupled with physical knowledge. This results in a hybrid state estimator based on a Kalman filter. This novel hybrid approach is presented using the example of estimating the roll angle of a vehicle.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"94 1","pages":"894-899"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91328204","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: 6

Distributed Model Predictive Intersection Control of Multiple Vehicles 多车辆分布式模型预测交叉口控制

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917117

M. Kloock, Patrick Scheffe, S. Marquardt, Janis Maczijewski, Bassam Alrifaee, S. Kowalewski

引用次数: 25

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network 基于视觉的深度残差神经网络变道行为检测

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917158

Zhensong Wei, Chao Wang, Peng Hao, M. Barth

{"title":"Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network","authors":"Zhensong Wei, Chao Wang, Peng Hao, M. Barth","doi":"10.1109/ITSC.2019.8917158","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917158","url":null,"abstract":"Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"28 1","pages":"3108-3113"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87169878","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: 17

Improving Map Re-localization with Deep ‘Movable’ Objects Segmentation on 3D LiDAR Point Clouds 基于3D LiDAR点云的深度“可移动”物体分割改进地图重新定位

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917390

Victor Vaquero, Kai Fischer, F. Moreno-Noguer, A. Sanfeliu, Stefan Milz

{"title":"Improving Map Re-localization with Deep ‘Movable’ Objects Segmentation on 3D LiDAR Point Clouds","authors":"Victor Vaquero, Kai Fischer, F. Moreno-Noguer, A. Sanfeliu, Stefan Milz","doi":"10.1109/ITSC.2019.8917390","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917390","url":null,"abstract":"Localization and Mapping is an essential component to enable Autonomous Vehicles navigation, and requires an accuracy exceeding that of commercial GPS-based systems. Current odometry and mapping algorithms are able to provide this accurate information. However, the lack of robustness of these algorithms against dynamic obstacles and environmental changes, even for short time periods, forces the generation of new maps on every session without taking advantage of previously obtained ones. In this paper we propose the use of a deep learning architecture to segment movable objects from 3D LiDAR point clouds in order to obtain longer-lasting 3D maps. This will in turn allow for better, faster and more accurate re-localization and trajectoy estimation on subsequent days. We show the effectiveness of our approach in a very dynamic and cluttered scenario, a supermarket parking lot. For that, we record several sequences on different days and compare localization errors with and without our movable objects segmentation method. Results show that we are able to accurately re-locate over a filtered map, consistently reducing trajectory errors between an average of 35.1% with respect to a non-filtered map version and of 47.9% with respect to a standalone map created on the current session.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"8 1","pages":"942-949"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87279672","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

Backpropagation through Simulation: A Training Method for Neural Network-based Car-following 基于仿真的反向传播:一种基于神经网络的汽车跟随训练方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917308

Ruoyu Sun, Donghao Xu, Huijing Zhao, M. Moze, F. Aioun, F. Guillemard

{"title":"Backpropagation through Simulation: A Training Method for Neural Network-based Car-following","authors":"Ruoyu Sun, Donghao Xu, Huijing Zhao, M. Moze, F. Aioun, F. Guillemard","doi":"10.1109/ITSC.2019.8917308","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917308","url":null,"abstract":"Learning human’s car-following behavior needs not only well-designed models but also effective training or calibration methods. Comparing with the vast amount of efforts on car-following modeling in literature, training methods are less studied. This research proposes a training method (BPTS - Backpropagation through Simulation) to reduce the long-term error of neural network-based car-following models, with multiple experimental validations. The training method uses a recurrent framework with simulation to generate long-term predictions for generic car-following models, and use gradient backpropagation to reduce accumulative error. The proposed training method can also calibrate other car-following models besides neural network-based models. In experimental validation, our studies yielded more than 30% error reduction in long-term (20 s) prediction for feed-forward Artificial Neural Network (ANN) and Long short-term memory (LSTM) models, and reduces the error on vehicle position by more than 1.0 meters, at the cost of that short-term (0.2 s) prediction error slightly increases. The proposed training method dramatically reduces the long-term prediction error of neural network-based car-following models.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"1 1","pages":"3796-3803"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87295975","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