2018 21st International Conference on Intelligent Transportation Systems (ITSC)最新文献

Real-time Driver Identification using Vehicular Big Data and Deep Learning 基于车辆大数据和深度学习的实时驾驶员识别

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569452

Daun Jeong, Minseok Kim, KyungTaek Kim, Tae-Won Kim, JiHun Jin, ChungSu Lee, Sejoon Lim

引用次数: 10

3D Ground Point Classification for Automotive Scenarios 汽车场景的3D地面点分类

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569898

Julia Nitsch, J. Aguilar, Juan I. Nieto, R. Siegwart, M. Schmidt, César Cadena

引用次数: 7

Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints 利用轨道几何约束提高列车定位精度

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569456

H. Winter, Volker Willert, J. Adamy

{"title":"Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints","authors":"H. Winter, Volker Willert, J. Adamy","doi":"10.1109/ITSC.2018.8569456","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569456","url":null,"abstract":"Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a track-selective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117161704","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

A Cognitive Framework for Unifying Human and Artificial Intelligence in Transportation Systems Modeling 交通系统建模中人类与人工智能统一的认知框架

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569322

J. Yu, R. Jayakrishnan

{"title":"A Cognitive Framework for Unifying Human and Artificial Intelligence in Transportation Systems Modeling","authors":"J. Yu, R. Jayakrishnan","doi":"10.1109/ITSC.2018.8569322","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569322","url":null,"abstract":"Humans, as indispensable components in any transportation systems, have been very challenging to model and predict, especially in hypothetical scenarios. Adding further complexity is the increasingly important role of artificial intelligence and rapidly changing technologies and business models. We propose a modeling framework, CognAgent, which unifies the modeling approach of different types of autonomous entities from the perspective of cognition rather than revealed behaviors. This approach improves model flexibility, interpretability, and computational efficiency. Heterogeneous agents inherit from a single blueprint agent and interact with one another within the Physical Interaction module, the output of which is fed into the module of Space of Observables for agents to sense and perceive through noisy media of information transmission. Combining with prior knowledge, preprogrammed routines, emotions, and habits, agents make decisions on how to act in the Physical Interaction module. In CognAgent, information is a result of the change of perceived uncertainty, and therefore, consistent with the Information Theory. Owing to this explicitness of agents' cognition, the derived models become extendable to new technology and business models. Equity analysis related to cognitive limitations such as vision and hearing loss becomes also natural. The numerical example models explicitly humans and autonomous vehicles with heterogeneous information transmission, perception, and risk preference.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117173492","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

Low-cost Lane-level Positioning in Urban Area Using Optimized Long Time Series GNSS and IMU Data 基于优化长时间序列GNSS和IMU数据的城市低成本车道水平定位

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569565

J. Meguro, T. Arakawa, Syunsuke Mizutani, Aoki Takanose

引用次数: 9

Incremental Learning Models of Bike Counts at Bike Sharing Systems 共享单车系统中自行车数量的增量学习模型

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569735

M. Almannaa, Mohammed Elhenawy, F. Guo, H. Rakha

{"title":"Incremental Learning Models of Bike Counts at Bike Sharing Systems","authors":"M. Almannaa, Mohammed Elhenawy, F. Guo, H. Rakha","doi":"10.1109/ITSC.2018.8569735","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569735","url":null,"abstract":"Bike sharing systems (BSSs) have become a convenient and environmentally friendly transportation mode, but may suffer from logistical issues such as bike shortages at stations. Predicting bike counts would help mitigate imbalances in the system. Research has focused on global prediction techniques but has neglected the role of user incentives. We adopted two computational techniques to capture BSS dynamics: mini-batch gradient descent for the linear regression (MBGDLR) and locally weighted regression (LWR). The two approaches used incremental learning based only on the previous status of the station with neither weather nor time information. The models were applied to a BSS data set for one year (2014–2015) in the San Francisco Bay Area for different prediction windows. Both models gave comparable results. LWR performed slightly better than MBGDLR for all prediction windows. The smallest prediction error for LWR was 0.31 bikes/station (4% prediction error) for a 15-minute prediction window and 0.32 bikes/station for MBGDLR. The 120-minute prediction window had the largest prediction error of 1.1 bikes/station and 1.2 bikes/station for LWR and MBGDLR, respectively. Computationally, MBGDLR was 55 times faster than LWR and proved to be faster than other machine learning and time series algorithms.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127532915","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

Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning 通过深度强化学习消除封闭和开放网络中的走走停停波

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569485

Abdul Rahman Kreidieh, Cathy Wu, A. Bayen

引用次数: 83

Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power 车载边缘云计算:减轻智能汽车车载计算能力的压力

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569286

Xin Li, Yifan Dang, Tefang Chen

{"title":"Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power","authors":"Xin Li, Yifan Dang, Tefang Chen","doi":"10.1109/ITSC.2018.8569286","DOIUrl":"https://doi.org/10.1109/ITSC.2018.8569286","url":null,"abstract":"Recently, with the rapid development of autonomous vehicles and connected vehicles, the demands of vehicular computing keep continuously growing. We notice a constant and limited onboard computational ability can hardly keep up with the rising requirements of the vehicular system and software application during their long-term lifetime, and also at the same time, the vehicles onboard computation causes an increasingly higher vehicular energy consumption. Therefore, we suppose to build a vehicular edge cloud computing (VECC) framework to resolve such a vehicular computing dilemma. In this framework, potential vehicular computing tasks can be executed remotely in an edge cloud within their time latency constraints. Simultaneously, an effective wireless network resources allocation scheme is one of the essential and fundamental factors for the QoS (quality of Service) on the VECC. In this paper, we adopted a stochastic fair allocation (SFA) algorithm to randomly allocate minimum required resource blocks to admitted vehicular users. The numerical results show a great effectiveness of energy efficiency in VECC.","PeriodicalId":395239,"journal":{"name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126129859","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: 9

Computationally Efficient Fail-safe Trajectory Planning for Self-driving Vehicles Using Convex Optimization 基于凸优化的自动驾驶汽车高效故障安全轨迹规划

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569425

Christian Pek, M. Althoff

引用次数: 45

Platoon Route Optimization for Picking up Automated Vehicles in an Urban Network 城市网络中自动驾驶车辆的排队路线优化

2018 21st International Conference on Intelligent Transportation Systems (ITSC) Pub Date : 2018-11-01 DOI: 10.1109/ITSC.2018.8569809

Mohamed Hadded, Jean-Marc Lasgouttes, F. Nashashibi, Ilias Xydias

引用次数: 1