2015 IEEE Intelligent Vehicles Symposium (IV)最新文献_第5页

Advanced path following control of an overactuated robotic vehicle 超驱动机器人车辆的高级路径跟踪控制

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225834

Peter Ritzer, C. Winter, J. Brembeck

引用次数: 12

Autonomous car following: A learning-based approach 自动驾驶汽车跟踪:基于学习的方法

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225802

S. Lefèvre, Ashwin Carvalho, F. Borrelli

引用次数: 60

Sideslip estimation for articulated heavy vehicles in low friction conditions 低摩擦条件下铰接式重型车辆的侧滑估计

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225664

Graeme Morrison, D. Cebon

引用次数: 6

Sampling recovery for closed loop rapidly expanding random tree using brake profile regeneration 基于制动轮廓再生的闭环快速扩展随机树采样恢复

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225670

Niclas Evestedt, Daniel Axehill, M. Trincavelli, F. Gustafsson

引用次数: 6

Inverse model control including actuator dynamics for active dolly steering in high capacity transport vehicle 大容量运输车辆小车主动转向的逆模型控制包括作动器动力学

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225819

M. Islam, L. Laine, B. Jacobson

引用次数: 11

Vehicle sensor and actuator fault detection algorithm for automated vehicles 自动驾驶汽车传感器和执行器故障检测算法

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225803

Yonghwan Jeong, Kyuwon Kim, Beomjun Kim, Jihyun Yoon, Hyok-Jin Chong, Bongchul Ko, K. Yi

引用次数: 14

Prioritizing collision avoidance and vehicle stabilization for autonomous vehicles 自动驾驶汽车的避碰和车辆稳定优先级

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225836

J. Funke, Matthew Brown, Stephen M. Erlien, J. C. Gerdes

{"title":"Prioritizing collision avoidance and vehicle stabilization for autonomous vehicles","authors":"J. Funke, Matthew Brown, Stephen M. Erlien, J. C. Gerdes","doi":"10.1109/IVS.2015.7225836","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225836","url":null,"abstract":"One approach to autonomous vehicle control is to generate and then track a desired trajectory without explicit consideration of vehicle stability. Stabilization is then entrusted to the vehicle's built-in production systems, such as electronic stability control, which constantly augment driving inputs to ensure stability. Other approaches explicitly consider stabilization criteria and implement permanently active constraints on the vehicle's actions. Situations exist, however, where enforcing stability constraints could lead to an otherwise avoidable collision. This paper presents an alternative paradigm for autonomous vehicle control that explicitly considers vehicle stability and environmental boundaries as it attempts to track a trajectory; such a mediator can choose to violate short term stability constraints in order to avoid a collision. Model predictive control provides an implementation framework, and an autonomous vehicle demonstrates the viability of the controller as it performs aggressive maneuvers. Driving around a turn at the vehicle's limits exhibits the importance of vehicle stability for autonomous vehicle control. Performing an emergency double lane change, however, highlights a situation where stability criteria must be temporarily violated to avoid a collision.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"16 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503279","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: 16

Feature-based mapping and self-localization for road vehicles using a single grayscale camera 基于单个灰度摄像机的道路车辆特征映射和自定位

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225697

M. Stuebler, J. Wiest, K. Dietmayer

{"title":"Feature-based mapping and self-localization for road vehicles using a single grayscale camera","authors":"M. Stuebler, J. Wiest, K. Dietmayer","doi":"10.1109/IVS.2015.7225697","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225697","url":null,"abstract":"This paper introduces a precise self-localization method for road vehicles. The presented approach is based on a single grayscale camera in addition with a conventional estimation of the ego motion and a map of the environment. This map is built in advance and independently from the localization process utilizing the same techniques. The proposed algorithm is based on Maximally Stable Extremal Regions which are robust features that are extracted from grayscale images. These features are matched in consecutive images using moment invariants. Together with an estimation of the ego motion, a 3D reconstruction of corresponding landmarks is obtained by applying multiple view geometry. For the unsupervised mapping process, landmarks are tracked and their corresponding global coordinates are stored in a geospatial database using a high-precision real-time kinematic system. The localization process itself is based on a particle filter to estimate the pose of the vehicle by making use of the previously generated map and currently observed landmarks. A standard GPS receiver is used to initialize the pose estimate. The evaluation with real world data shows that this approach achieves very good results despite the marginal sensor setup.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129964701","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: 13

Pedestrian orientation classification utilizing single-chip coaxial RGB-ToF camera 利用单片机同轴RGB-ToF摄像机进行行人方向分类

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225654

Fumito Shinmura, Yasutomo Kawanishi, Daisuke Deguchi, I. Ide, H. Murase, H. Fujiyoshi

引用次数: 2

Essential feature extraction of driving behavior using a deep learning method 使用深度学习方法提取驾驶行为的基本特征

2015 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2015-08-27 DOI: 10.1109/IVS.2015.7225824

Hailong Liu, T. Taniguchi, Yusuke Tanaka, Kazuhito Takenaka, T. Bando

{"title":"Essential feature extraction of driving behavior using a deep learning method","authors":"Hailong Liu, T. Taniguchi, Yusuke Tanaka, Kazuhito Takenaka, T. Bando","doi":"10.1109/IVS.2015.7225824","DOIUrl":"https://doi.org/10.1109/IVS.2015.7225824","url":null,"abstract":"Driving behavior can be represented by many different types of measured sensor information obtained through a control area network. We assume that the measured sensor information is generated from several hidden time-series data through multiple nonlinear transformations. These hidden time-series data are statistically independent of each other and capture essential driving behavior. Driving behavior information is usually generated by multiple nonlinear transformations that fuse essential features, e.g., \"Yaw rate\" is generated by fusing the velocity of the vehicle and the change of driving direction. However, driving behavior data is often redundant because such data includes multivariate information and involves duplicated essential features. In this paper, we propose a feature extraction method to extract essential features from redundant driving behavior data using a deep sparse autoencoder (DSAE), which is a deep learning method. Two-dimensional features are extracted from seven-dimensional artificial data using a DSAE and are determined experimentally to be highly correlated with the prepared essential features. DSAEs are also used to extract features from an actual driving behavior data set. To verify a DSAE's ability to extract essential driving behavior features and filter out redundant information, we prepare twelve data sets that include some or all of the driving behavior information. Twelve DSAEs are used to independently extract features from the twelve prepared data sets, and canonical correlation analysis is used to analyze the canonical correlation coefficients between extracted features. Furthermore, we verify DSAEs' ability to extract essential driving behavior features from the redundant driving behavior data sets.","PeriodicalId":294701,"journal":{"name":"2015 IEEE Intelligent Vehicles Symposium (IV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749553","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: 18