2017 IEEE Intelligent Vehicles Symposium (IV)最新文献_第10页

Automated mosaicing for improving vehicle situational awareness in real time 用于提高车辆实时态势感知的自动拼接

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995867

David Nam, N. Aouf

引用次数: 1

A learning model for personalized adaptive cruise control 个性化自适应巡航控制的学习模型

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995748

Xin Chen, Yong Zhai, Chao Lu, Jian-wei Gong, G. Wang

引用次数: 22

Road friction estimation for connected vehicles using supervised machine learning 基于监督机器学习的网联车辆道路摩擦估计

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995885

G. Panahandeh, Erik Ek, N. Mohammadiha

引用次数: 24

Lateral Model Predictive Control for Over-Actuated Autonomous Vehicle 过度驱动自动驾驶汽车横向模型预测控制

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995737

Gonçalo Collares Pereira, Lars Svensson, P. Lima, J. Mårtensson

引用次数: 19

Analysis of ITS-G5A V2X communications performance in autonomous cooperative driving experiments 自主协同驾驶实验中ITS-G5A V2X通信性能分析

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995982

I. Parra, Alvaro Garcia-Morcillo, R. Izquierdo, J. Alonso, D. F. Llorca, M. Sotelo

{"title":"Analysis of ITS-G5A V2X communications performance in autonomous cooperative driving experiments","authors":"I. Parra, Alvaro Garcia-Morcillo, R. Izquierdo, J. Alonso, D. F. Llorca, M. Sotelo","doi":"10.1109/IVS.2017.7995982","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995982","url":null,"abstract":"In this paper the performance of ITS-G5A communications for an autonomous driving application is analyzed in a real high-density scenario. The data was collected during the cooperative platooning tests that took place in Helmond in the frame of the Grand Cooperative Driving Challenge 2016. In the competition, between 8–10 autonomous vehicles formed two platoons in different lanes and were required to merge into a predefined competition zone. The performance is characterized using CAM CCDFs which serves as a base for the evaluation of a Cooperative Adaptive Cruise Control application. Two important effects has been identified that affect to the reliability of the communications. Firstly, there is a degradation with the distance that appears to be stronger for cars and more gentle for trucks. This indicates that occlusions heavily affect the connectivity of ITS-G5A. Secondly, the reliability is below expectations and some of the vehicles perform consistently worse than others. Although further investigation is required, a possible explanation for this is that a highly congested channel is making some of the vehicles get stuck and are not able to regularly access the channel.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124232438","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

Robust vehicle environment reconstruction from point clouds for irregularity detection 基于点云的鲁棒车辆环境重建，用于不规则检测

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995858

A. C. Sidiya, A. Rubaiyat, Y. P. Fallah, G. Bansal, Takayuki Shimizu, X. Li

{"title":"Robust vehicle environment reconstruction from point clouds for irregularity detection","authors":"A. C. Sidiya, A. Rubaiyat, Y. P. Fallah, G. Bansal, Takayuki Shimizu, X. Li","doi":"10.1109/IVS.2017.7995858","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995858","url":null,"abstract":"Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. Knowledge about the vehicle environment could facilitate reliable detection of moving objects, especially irregular events (e.g., pedestrians crossing the road, vehicles making sudden lane changes,) for the purpose of avoiding collisions. Inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-ICP technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle. Moreover, we propose to address the issue of irregularity detection from the extracted moving objects. Our experimental results have shown successful reconstruction of a variety of challenging vehicle environments (including rural and urban, road and intersection, etc.) and simultaneous tracking/segmentation of multiple moving objects.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124420979","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

Baidu driving dataset and end-to-end reactive control model 百度驱动数据集和端到端响应式控制模型

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995742

Hao Yu, Shu Yang, Weihao Gu, Shaoyu Zhang

{"title":"Baidu driving dataset and end-to-end reactive control model","authors":"Hao Yu, Shu Yang, Weihao Gu, Shaoyu Zhang","doi":"10.1109/IVS.2017.7995742","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995742","url":null,"abstract":"End-to-end autonomous driving system has obtained great progress recently. In this paper, we will introduce our open source dataset: Baidu Driving Dataset(BDD), and our end-to-end reactive control model trained on BDD. The BDD comes from Baidu street view project, which generates millions of kilometers driving data every year. Among them, we publish 10000 kilometers driving data for end-to-end autonomous driving research. The BDD consists of two parts: forward images and vehicle motion attitude. The vehicle motion attitude is derived from real time kinematic GPS location data with standard deviation of 3 centimeters. Our reactive control model consists of lateral control and longitudinal control. We employ curvature instead of steering angle for lateral control, and leverage acceleration, not throttle or brake, for longitudinal control. CNN network is employed for lateral control model, mapping a single image from forward camera directly to corresponding curvature. For longitudinal control, stacked convolutional LSTM is used to extract spatial and temporal features from a sequence of frames, and to map the features with longitudinal control commands. The demo and data are in http://roadhackers.baidu.com. To the best of our knowledge, it is the first time that both lateral and longitudinal control are implemented in an end-to-end style.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375818","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: 31

Real-time method for general road segmentation 一般道路分割的实时方法

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995758

Michelle Valente, B. Stanciulescu

引用次数: 7

Guided depth upsampling for precise mapping of urban environments 用于精确绘制城市环境的引导深度上采样

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995866

Sascha Wirges, Björn Roxin, Eike Rehder, T. Kühner, M. Lauer

引用次数: 8

Ego-lane estimation for downtown lane-level navigation 市区车道级导航的自我车道估计

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995868

Johannes Rabe, M. Hubner, M. Necker, C. Stiller

引用次数: 7