2019 IEEE Intelligent Transportation Systems Conference (ITSC)最新文献_第7页

A microsimulation approach for the impact assessment of a Vehicle-to-Infrastructure based Road Hazard Warning system 基于车辆对基础设施的道路危险预警系统影响评估的微观模拟方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917350

Kallirroi N. Porfyri, Areti Kotsi, E. Mitsakis

{"title":"A microsimulation approach for the impact assessment of a Vehicle-to-Infrastructure based Road Hazard Warning system","authors":"Kallirroi N. Porfyri, Areti Kotsi, E. Mitsakis","doi":"10.1109/ITSC.2019.8917350","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917350","url":null,"abstract":"Cooperative Intelligent Transportation Systems (C-ITS) rely on the use of communication technologies to enable vehicles to exchange information with other vehicles, roadside infrastructure, back-end centres and mobile devices. Verification or testing is required for C-ITS applications, in order to assess their impact on traffic operation. Microsimulation appears to be a robust tool that allows to gain insights into the implementation and performance of such systems. In this work, a microscopic traffic simulation approach is used, to evaluate the impact of Vehicle-to-Infrastructure (V2I) technologies in the context of a road traffic accident. Specifically, the methodology is implemented to study a Road Hazard Warning (RHW) system, using the open source microscopic traffic simulator SUMO. The approach explicitly models vehicles collisions, RHW, Emergency Electronic Brake Light (EEBL) warnings and the resulting driver behavior. Moreover, a new gap control mechanism is adopted, to improve safety by advising vehicles in hazard lane to increase their headways with respect to their preceding vehicle, so that they can avoid a collision. Perfect communication links to all vehicles are assumed. The study findings indicate that the proposed V2I hazard warning strategy has a positive impact on traffic flow safety and efficiency.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"101 1","pages":"4443-4448"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79352092","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

Personalized Safety-focused Control by Minimizing Subjective Risk 最小化主观风险的个性化安全控制

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917457

Naren Bao, Dongfang Yang, Alexander Carballo, Ü. Özgüner, K. Takeda

{"title":"Personalized Safety-focused Control by Minimizing Subjective Risk","authors":"Naren Bao, Dongfang Yang, Alexander Carballo, Ü. Özgüner, K. Takeda","doi":"10.1109/ITSC.2019.8917457","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917457","url":null,"abstract":"We propose a data-driven control framework for autonomous driving which combines learning-based risk assessment with personalized, safety-focused, predictive control. Different control strategies are used depending on the detected risk level of the driving situation (risky vs. non-risky). This requires a model which can understand the context of the driving situation. In addition, autonomous driving should also be able to provide various safe and comfortable driving styles customized for various users, which requires a modeling method that can capture individual driving preferences. To achieve this, we propose a novel vehicle control framework in which Model Predictive Control (MPC) is combined with a learning-based risk assessment model. Random Forest (RF) methods are trained to classify driving scenes as risky or not risky, while at the same time capturing individually preferred travel velocities. If driving scenes are classified as risky, then the Safety-focused Model Predictive Control (SMPC) system will be launched to generate control commands satisfying predetermined safety constraints, otherwise, Personalized Model Predictive Control (PMPC) is used instead to track the driver’s individually preferred velocity. We demonstrate experimentally our control framework.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"92 1","pages":"3853-3858"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81617406","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: 10

Multi-Reward Architecture based Reinforcement Learning for Highway Driving Policies 基于多奖励体系结构的公路驾驶策略强化学习

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917304

Wei Yuan, Ming Yang, Yuesheng He, Chunxiang Wang, B. Wang

{"title":"Multi-Reward Architecture based Reinforcement Learning for Highway Driving Policies","authors":"Wei Yuan, Ming Yang, Yuesheng He, Chunxiang Wang, B. Wang","doi":"10.1109/ITSC.2019.8917304","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917304","url":null,"abstract":"A safe and efficient driving policy is essential for the future autonomous highway driving. However, driving policies are hard for modeling because of the diversity of scenes and uncertainties of the interaction with surrounding vehicles. The state-of-the-art deep reinforcement learning method is unable to learn good domain knowledge for highway driving policies using single reward architecture. This paper proposes a Multi-Reward Architecture (MRA) based reinforcement learning for highway driving policies. A single reward function is decomposed to multi-reward functions for better representation of multi-dimensional driving policies. Besides the big penalty for collision, the overall reward is decomposed to three dimensional rewards: the reward for speed, the reward for overtake, and the reward for lane-change. Then, each reward trains a branch of Q-network for corresponding domain knowledge. The Q-network is divided into two parts: low-level network is shared by three branches of high-level networks, which approximate the corresponding Q-value for the different reward functions respectively. The agent car chooses the action based on the sum of Q vectors from three branches. Experiments are conducted in a simulation platform, which performs the highway driving process and the agent car is able to provide the commonly used sensor data: the image and the point cloud. Experiment results show that the proposed method performs better than the DQN method on single reward architecture with three evaluations: higher speed, lower frequency of lane-change, more quantity of overtaking, which is more efficient and safer for the future autonomous highway driving.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"24 1","pages":"3810-3815"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81618661","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: 8

Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing 基于增量机器学习和超维计算的城市道路交通轨迹聚类研究

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917320

T. Bandaragoda, Daswin De Silva, D. Kleyko, Evgeny Osipov, U. Wiklund, D. Alahakoon

{"title":"Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing","authors":"T. Bandaragoda, Daswin De Silva, D. Kleyko, Evgeny Osipov, U. Wiklund, D. Alahakoon","doi":"10.1109/ITSC.2019.8917320","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917320","url":null,"abstract":"Road traffic congestion in urban environments poses an increasingly complex challenge of detection, profiling and prediction. Although public policy promotes transport alternatives and new infrastructure, traffic congestion is highly prevalent and continues to be the lead cause for numerous social, economic and environmental issues. Although a significant volume of research has been reported on road traffic prediction, profiling of traffic has received much less attention. In this paper we address two key problems in traffic profiling by proposing a novel unsupervised incremental learning approach for road traffic congestion detection and profiling, dynamically over time. This approach uses (a) hyperdimensional computing to enable capture variable-length trajectories of commuter trips represented as vehicular movement across intersections, and (b) transforms these into feature vectors that can be incrementally learned over time by the Incremental Knowledge Acquiring Self-Learning (IKASL) algorithm. The proposed approach was tested and evaluated on a dataset consisting of approximately 190 million vehicular movement records obtained from 1,400 Bluetooth identifiers placed at the intersections of the arterial road network in the State of Victoria, Australia.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"12 1","pages":"1664-1670"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81883260","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: 28

Online Conformance Testing of CBTC On-board ATO Functions Based on UPPAAL-TRON Framework 基于UPPAAL-TRON框架的CBTC机载ATO功能在线一致性测试

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917035

Kehang Chen, J. Lv, Jia Huang, Haonan Guo, S. Su, T. Tang

引用次数: 1

An Approach for CNN-Based Feature Matching Towards Real-Time SLAM 一种面向实时SLAM的cnn特征匹配方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917293

Marc Sons, Christian Kinzig, Dominic Zanker, C. Stiller

{"title":"An Approach for CNN-Based Feature Matching Towards Real-Time SLAM","authors":"Marc Sons, Christian Kinzig, Dominic Zanker, C. Stiller","doi":"10.1109/ITSC.2019.8917293","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917293","url":null,"abstract":"Matching keypoints between images showing the same scene under different conditions is a fundamental step for a variety of applications. Recent approaches based on convolutional neural networks show superior results in terms of discriminability compared to well established descriptors like SIFT or ORB. However, there is less previous work which brings the CNNs to automated driving applications like SLAM and analyze the performance in terms of accuracy and runtime. In this work, we take state-of-the-art patch comparison CNNs, train them from scratch and analyze the performance on the KITTI odometry benchmark. For that, we replace the ORBfrontend within the publicly available ORB-SLAM2 framework through our trained CNN variants and compare both. We show that it is necessary to downsize the complexity of the original architectures to achieve real-time capability. Furthermore, our evaluation shows that the downsized models achieve significantly higher matching performance than the ORB descriptor. Moreover, we achieve slightly better results on the KITTI odometry benchmark compared to ORB-SLAM2 while using a CNN-based feature descriptor, which can easily be adapted to different environments.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"88 1","pages":"1305-1310"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79440736","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: 2

Dynamic Origin-Destination Estimation Framework with Iterative Traffic Signal Tuning for Microscopic Traffic Simulation 基于迭代交通信号调谐的微观交通仿真动态始发-目的地估计框架

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917219

Yu Wang, Yicheng Zhang, Hai-Heng Ng, Bing Zhao, W. Ng

{"title":"Dynamic Origin-Destination Estimation Framework with Iterative Traffic Signal Tuning for Microscopic Traffic Simulation","authors":"Yu Wang, Yicheng Zhang, Hai-Heng Ng, Bing Zhao, W. Ng","doi":"10.1109/ITSC.2019.8917219","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917219","url":null,"abstract":"To validate traffic signal control algorithm’s performance, a setup of microscopic traffic simulation platform with realistic traffic demand is necessary. Traditionally, a bilevel framework of Origin-Destination (OD) calibration and trip assignment, is setup to estimate OD so that realistic traffic demand can be emulated in simulation platform. However, with this approach, we may mislead the calibration process by introducing insufficient green time allocation, as vehicles are likely to be stopped by red signals and thus vehicle throughput will never reach the real traffic demand. While this happens occasionally in unsaturated traffic condition, it is very prevalent in the saturated condition scenario. This paper introduces a trilevel problem formulation with consideration of traffic signal schedules during the OD estimation process. The first level uses an iterative algorithm (LSQR) to generate OD traffic demand with certain constraints based on real loop count data at junctions. Second level applies the traffic demand into a simulation platform to generate the trips between OD points. Dynamic User Equilibrium (DUE) will be satisfied iteratively so that the trip assignment is reasonable. Finally, the third level applies Iterative Tuning (IT) signal controller to tune signal schedules iteratively, such that sufficient green time can be allocated to allow vehicles drive through intersections. Via OD calibrations in corridor and area networks, we show that the trilevel OD estimation approach can achieve better performance as compared to the bi-level approach.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"1 1","pages":"2201-2206"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79779659","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

An Interval Algebraic Approach for vehicle lateral tire forces estimation 车辆横向轮胎力估计的区间代数方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917146

S. Ifqir, D. Ichalal, N. A. Oufroukh, S. Mammar

引用次数: 2

TriP: Misbehavior Detection for Dynamic Platoons using Trust 基于信任的动态队列的错误行为检测

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917188

Keno Garlichs, Alexander Willecke, M. Wegner, L. Wolf

引用次数: 11

Analysis of the Relationship Between Physiological Signals and Vehicle Maneuvers During a Naturalistic Driving Study 自然驾驶研究中生理信号与车辆动作的关系分析

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917198

Yuning Qiu, Teruhisa Misu, C. Busso

{"title":"Analysis of the Relationship Between Physiological Signals and Vehicle Maneuvers During a Naturalistic Driving Study","authors":"Yuning Qiu, Teruhisa Misu, C. Busso","doi":"10.1109/ITSC.2019.8917198","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917198","url":null,"abstract":"As a driver prepares to complete a maneuver, his/her internal cognitive state triggers physiological responses that are manifested, for example, in changes in heart rate (HR), breath rate (BR), and electrodermal activity (EDA). This process opens opportunities to understand driving events by observing the physiological data of the driver. In particular, this work studies the relation between driver maneuvers and physiological signals during naturalistic driving recordings. It presents both feature and discriminant analysis to investigate how physiological data can signal driver’s responses for planning, preparation, and execution of driving maneuvers. We study recordings with extreme values in the physiological data (high and low values in HR, BR, and EDA). The analysis indicates that most of these events are associated with driving events. We evaluate the values obtained from physiological signals as the driver complete specific maneuvers. We observe deviations from typical physiological responses during normal driving recordings that are statistically significant. These results are validated with binary classification problems, where the task is to recognize between a driving maneuver and a normal driving condition (e.g., left turn versus normal). The average F1-score of these classifiers is 72.8%, demonstrating the discriminative power of features extracted from physiological signals.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"38 1","pages":"3230-3235"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85327136","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: 10