2017 IEEE Intelligent Vehicles Symposium (IV)最新文献_第7页

Fully convolutional neural networks for dynamic object detection in grid maps 网格地图中动态目标检测的全卷积神经网络

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995750

Florian Piewak, Timo Rehfeld, Michael Weber, Johann Marius Zöllner

引用次数: 19

Real-time velocity planning for heavy duty truck with obstacle avoidance 重型卡车避障实时速度规划

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995706

Mahdi Morsali, E. Frisk, J. Åslund

引用次数: 11

Estimation of collective maneuvers through cooperative multi-agent planning 基于协同多智能体规划的集体机动估计

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995788

Jens Schulz, Kira Hirsenkorn, Julian Löchner, M. Werling, Darius Burschka

引用次数: 18

Assessing driver cortical activity under varying levels of automation with functional near infrared spectroscopy 用功能性近红外光谱法评估不同自动化水平下驾驶员皮层活动

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995923

S. Sibi, Stephanie Balters, Brian K. Mok, M. Steinert, Wendy Ju

{"title":"Assessing driver cortical activity under varying levels of automation with functional near infrared spectroscopy","authors":"S. Sibi, Stephanie Balters, Brian K. Mok, M. Steinert, Wendy Ju","doi":"10.1109/IVS.2017.7995923","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995923","url":null,"abstract":"Information about drivers' mental states can be vital to the design of interfaces for highly automated vehicles. Functional near infrared spectroscopy (fNIRS) is a neuroimaging tool that is fast becoming popular to study the cortical activity of participants in HCI experiments and driving simulator studies in particular. The analysis methods of the fNIRS data create requirements in the experimental design such as repeated measures. In this paper, we present a study of the event related cortical activity of the drivers of manual, partially autonomous, and fully autonomous cars when performing lane changes using functional near infrared spectroscopic measures. We also present the experimental methodology that was adopted to meet the needs of the fNIRS measurement and the subsequent analysis. The study (N=28) was conducted in a driving simulator. Participants drove for approximately 7 minutes and performed 8 lane change maneuvers in each mode of automation. Multiple streams of data including 4 time-synced video recordings, NASA TLX questionnaires and fNIRS data were recorded and analyzed. It was found that the dorsolateral prefrontal cortex activation during lane changes performed in a partially autonomous mode of operation was just as high as that during a manual lane change, showing that drivers of partially automated systems are as cognitively engaged as drivers of manually operated vehicles.","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":"121110374","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: 12

Pose estimation with lidar odometry and cellular pseudoranges 利用激光雷达里程计和细胞伪距进行位姿估计

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995956

Joe J. Khalife, S. Ragothaman, Z. Kassas

引用次数: 16

Free-space detection with fish-eye cameras 用鱼眼相机进行自由空间探测

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995710

Simon Hanisch, Rubén Heras Evangelio, H. Tadjine, Michael Pätzold

引用次数: 7

Observer-based controller with integral action for longitudinal vehicle speed control 车辆纵向速度控制的观测器积分控制

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995739

B. Boulkroune, S. V. Aalst, Kris Lehaen, J. D. Smet

引用次数: 7

Distributed robust vehicle state estimation 分布式鲁棒车辆状态估计

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995798

E. Hashemi, Mohammad Pirani, B. Fidan, A. Khajepour, Shih-Ken Chen, B. Litkouhi

引用次数: 4

Real-time hand posture and gesture-based touchless automotive user interface using deep learning 使用深度学习的实时手部姿势和基于手势的非接触式汽车用户界面

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995825

V. John, Makoto Umetsu, Ali Boyali, S. Mita, Masayuki Imanishi, Norio Sanma, Syunsuke Shibata

{"title":"Real-time hand posture and gesture-based touchless automotive user interface using deep learning","authors":"V. John, Makoto Umetsu, Ali Boyali, S. Mita, Masayuki Imanishi, Norio Sanma, Syunsuke Shibata","doi":"10.1109/IVS.2017.7995825","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995825","url":null,"abstract":"In this study, a vision based in-car entertainment user interface is presented. The user interface is designed using a hand posture and gesture recognition algorithm in deep learning framework. The hand posture recognition algorithm is formulated using the convolutional neural network to perform the fundamental tasks in the user interface. The hand gesture recognition algorithm is formulated using the long-term recurrent convolutional neural network to intuitively interact with the touchless automotive user interface in a detailed manner. In the recurrent deep learning framework, typically, the gesture frames are taken from a uniformly sampled image sequence. In this work, the recurrent structure is enhanced using a reduced number of input frames captured from the image sequence. The reduced input frames or key frames represent the action present in the video sequence. Sparse dictionary learning provide reliable key frame extraction from video sequences. However, sparse dictionary learning is computationally expensive, and are individually optimized for every video sequence. In this paper, we propose to approximate sparse dictionary learning using a non-linear regression framework. The multilayer perceptron is utilized to model the non-linear regression framework. The optimal neural network architecture is identified after a detailed evaluation. We evaluate the proposed recognition methods on public datasets. The proposed methods yield a recognition accuracy of 92% and 90% for pose and gestures, respectively. The combined hand posture and gesture recognition takes 82ms which is a reasonable for real time implementation.","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":"125302428","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

Entering crossroads with blind corners. A safe strategy for autonomous vehicles 进入有死角的十字路口。自动驾驶汽车的安全策略

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995803

S. Hörmann, Felix Kunz, Dominik Nuss, Stephan Reuter, K. Dietmayer

引用次数: 29