2019 IEEE Intelligent Transportation Systems Conference (ITSC)最新文献_第6页

How Do Passing Events Influence the Perception of Risk Among Cyclistsƒ 过往事件如何影响骑车者的风险感知

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916976

T. M. Hale, R. Doorley, Michael O'Byrne, Giacinto Rittgers, V. Pakrashi, Bidisha Ghosh

引用次数: 4

Vision Based Localization for Infrastructure Enabled Autonomy 基于视觉的基础设施自治定位

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916896

Deepika Ravipati, Kenny Chour, Abhishek Nayak, T. Marr, Sheelabhadra Dey, Alvika Gautam, S. Rathinam, Swaminathan Gopalswamy

{"title":"Vision Based Localization for Infrastructure Enabled Autonomy","authors":"Deepika Ravipati, Kenny Chour, Abhishek Nayak, T. Marr, Sheelabhadra Dey, Alvika Gautam, S. Rathinam, Swaminathan Gopalswamy","doi":"10.1109/ITSC.2019.8916896","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916896","url":null,"abstract":"Infrastructure Enabled Autonomy (IEA) is a new paradigm in autonomous vehicles research that aims at distributed intelligence architecture by transferring the core functionalities of sensing and localization to infrastructure. This paradigm is also promising in designing scalable systems that enable autonomous car platooning on highways. This paper gives a detailed description about the experimental realization of IEA and techniques devised to localize a vehicle in such a setup. A reliable camera calibration technique for such an experimental setup is discussed, followed by a technique to transform 2D image coordinates to 3D world coordinates. In this research, localization information is received from on-board vehicle sensors like GPS/IMU, and (2) localized vehicle position data derived from deep learning, and 2D to 3D coordinate transformations on the real-time camera feeds and (3) lane detection data from infrastructure cameras. This data is fused together utilizing an Extended Kalman Filter (EKF) to obtain reliable estimates of the position of the vehicle at 50 Hz. This position information is then used to control the vehicle with an objective of following a prescribed path. Extensive simulation and experimental results are also presented to corroborate the performance of the proposed approach.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"49 1","pages":"1638-1643"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74809112","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: 5

A Simulation Modeling Framework with Autonomous Vehicle Region-based Routing and Public Transit Diversion Integration 基于区域的自动驾驶车辆路径与公共交通导流集成仿真建模框架

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917309

S. Ware, Antonis F. Lentzakis, R. Su

{"title":"A Simulation Modeling Framework with Autonomous Vehicle Region-based Routing and Public Transit Diversion Integration","authors":"S. Ware, Antonis F. Lentzakis, R. Su","doi":"10.1109/ITSC.2019.8917309","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917309","url":null,"abstract":"In this paper, we present a simulation modeling framework that can accommodate multiple classes of travelers and integrates several distinct features, which in turn can be associated with each of the traveler classes, thus providing flexibility and a so-called bird’s-eye view to any potential user. Concretely, we integrate into the multi-class region-based dynamic traffic model, called multi-class Network Transmission Model (McNTM), several features, including a public transit diversion component, as well as routing methods associated with different traveler classes. Three distinct traveler classes are defined, the 1st class of travelers equipped with autonomous vehicles, the 2nd traveler class comprising of RGIS-equipped, conventional vehicles and the 3rd traveler class comprising of unequipped, conventional vehicles. Certain assumptions are made for each traveler class. The gain in overall performance for the case where 1st and 2nd class travelers are present in the system, ranges from 0.78% - 23.43%. Region-based routing methods employed by the 1st and 2nd class respectively, not only benefit overall network performance, but with their respective market penetration rates exceeding certain thresholds, can prove beneficial to the individual performance of other traveler classes.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"55 1","pages":"2626-2632"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72856622","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

Manual Drivers’ Evaluation of Automated Merging Behavior in Dense Traffic: Efficiency Matters 密集交通中手动驾驶员对自动归并行为的评价:效率问题

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917346

Johannes Potzy, Sophie Feinauer, Karl-Heinz Siedersberger, K. Bengler

{"title":"Manual Drivers’ Evaluation of Automated Merging Behavior in Dense Traffic: Efficiency Matters","authors":"Johannes Potzy, Sophie Feinauer, Karl-Heinz Siedersberger, K. Bengler","doi":"10.1109/ITSC.2019.8917346","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917346","url":null,"abstract":"The vision to integrate automated vehicles into manual traffic motivates to investigate automated merging in dense traffic. To gain an easy, distinct and interpretable behavior for interacting traffic this study investigates release conditions of lane changes into small gaps in a within-subject design on a test track with 39 participants. To generate standardized situations all merging maneuvers are performed automatically. The study is divided into two parts. In the first part participants validate different times headway between the participant’s vehicle and automated vehicle under different situational parameters (deceleration to target gap, velocity and existence of road work). In the second part, participants release the lane change of the automated vehicle themselves, when they expected it to merge. Here, in addition to part one, the automated vehicle adjusted velocity to the target gap with weak and strong deceleration. The results show that participants prefer an efficient lane change of the automatic vehicle, where interacting traffic has to react as little as possible. Compliance with safety distances is not decisive. The required times headway between automated and interacting vehicles decreases with higher velocity and in lane narrowing situations. The study contributes to the design of vehicle behaviour that can enhance the acceptance of automated vehicles in mixed-traffic.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"86 1","pages":"3454-3460"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73051185","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

Lane-Merging Using Policy-based Reinforcement Learning and Post-Optimization 基于策略强化学习和后优化的车道合并

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917002

Patrick Hart, Leonard Rychly, A. Knoll

引用次数: 13

Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning 基于分层规划和强化学习的多车道巡航

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916928

K. Rezaee, P. Yadmellat, M. Nosrati, E. Abolfathi, Mohammed Elmahgiubi, Jun Luo

{"title":"Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning","authors":"K. Rezaee, P. Yadmellat, M. Nosrati, E. Abolfathi, Mohammed Elmahgiubi, Jun Luo","doi":"10.1109/ITSC.2019.8916928","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8916928","url":null,"abstract":"Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction. While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action space according to high level behavioral decisions. We argue that this design allows principled modular extension of motion planning, in contrast to using either monolithic behavior cloning or a large set of handwritten rules. Moreover, we demonstrate that our state-action space abstraction allows transferring of the trained models without retraining from a simulated environment with virtually no dynamics to one with significantly more realistic dynamics. Together, these results suggest that our proposed hierarchical architecture is a promising way to allow reinforcement learning to be applied to complex multi-lane cruising in the real world.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"32 1","pages":"1800-1806"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73180753","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

Dynamic States Prediction in Autonomous Vehicles: Comparison of Three Different Methods 自动驾驶汽车动态状态预测:三种不同方法的比较

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8916969

Teng Liu, Bin Tian, Yunfeng Ai, Long Chen, Fei Liu, Dongpu Cao, Ning Bian, Feiyue Wang

引用次数: 6

Coarse-to-Fine Luminance Estimation for Low-Light Image Enhancement in Maritime Video Surveillance 海上视频监控中微光图像增强的粗到细亮度估计

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917151

Meifang Yang, Xin Nie, R. W. Liu

{"title":"Coarse-to-Fine Luminance Estimation for Low-Light Image Enhancement in Maritime Video Surveillance","authors":"Meifang Yang, Xin Nie, R. W. Liu","doi":"10.1109/ITSC.2019.8917151","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917151","url":null,"abstract":"Captured images in maritime video surveillance under non-uniform illumination conditions easily suffer from low contrast and details loss. The low-quality images may significantly result in negative effects in practical applications, e.g., target detection, recognition, classification and tracking, etc. Increasing attention has recently been paid to improve the quality of low-light images via computer vision techniques. In this paper, we propose to establish a two-step luminance estimation framework to enhance low-light images. In particular, the coarse luminance is firstly estimated using traditional Max-RGB which extracts the highest pixel values in each color channel. The refined luminance is obtained by introducing a weighted variational model which has the capacities of structure-preserving and texture-smoothing. Based on the estimated well-constructed luminance, the enhanced low-light images are obtained by combining Retinex model with its extended version. The image quality is further improved through a BM3D-based denoising approach. Experimental results on both synthetic and realistic low-light images have demonstrated the satisfactory imaging performance in terms of quantitative and qualitative evaluations.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"42 4","pages":"299-304"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72596325","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: 21

Estimation of Traffic Demand Corresponding to Observed Link Traffic Volume in Microscopic Simulation 微观仿真中观测到的链路交通量对应的交通需求估计

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917275

K. Abe, H. Fujii, S. Yoshimura

引用次数: 0

A Method for Synthetic LiDAR Generation to Create Annotated Datasets for Autonomous Vehicles Perception 一种合成激光雷达生成自动驾驶汽车感知注释数据集的方法

2019 IEEE Intelligent Transportation Systems Conference (ITSC) Pub Date : 2019-10-01 DOI: 10.1109/ITSC.2019.8917176

Jorge Beltrán, Irene Cortés, Alejandro Barrera, Jesús Urdiales, Carlos Guindel, F. García, A. D. L. Escalera

{"title":"A Method for Synthetic LiDAR Generation to Create Annotated Datasets for Autonomous Vehicles Perception","authors":"Jorge Beltrán, Irene Cortés, Alejandro Barrera, Jesús Urdiales, Carlos Guindel, F. García, A. D. L. Escalera","doi":"10.1109/ITSC.2019.8917176","DOIUrl":"https://doi.org/10.1109/ITSC.2019.8917176","url":null,"abstract":"LiDAR devices have become a key sensor for autonomous vehicles perception due to their ability to capture reliable geometry information. Indeed, approaches processing LiDAR data have shown an impressive accuracy for 3D object detection tasks, outperforming methods solely based on image inputs. However, the wide diversity of on-board sensor configurations makes the deployment of published algorithms into real platforms a hard task, due to the scarcity of annotated datasets containing laser scans. We present a method to generate new point clouds datasets as captured by a real LiDAR device. The proposed pipeline makes use of multiple frames to perform an accurate 3D reconstruction of the scene in the spherical coordinates system that enables the simulation of the sweeps of a virtual LiDAR sensor, configurable both in location and inner specifications. The similarity between real data and the generated synthetic clouds is assessed through a set of experiments performed using KITTI Depth and Object Benchmarks.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"239 1","pages":"1091-1096"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73963497","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