2017 IEEE Intelligent Vehicles Symposium (IV)最新文献_第5页

Anomaly detection of CAN bus messages through analysis of ID sequences 通过分析ID序列实现CAN总线消息的异常检测

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995934

Mirco Marchetti, Dario Stabili

引用次数: 153

Long short term memory for driver intent prediction 驾驶员意图预测的长短期记忆

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995919

Alex Zyner, Stewart Worrall, James R. Ward, E. Nebot

引用次数: 103

The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles? 运动学自行车模型:自动驾驶汽车可行轨迹规划的一致模型?

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995816

Philip Polack, Florent Altché, B. d'Andréa-Novel, A. D. L. Fortelle

引用次数: 239

Traffic scene segmentation based on boosting over multimodal low, intermediate and high order multi-range channel features 基于多模态低、中、高阶多范围通道特征的增强交通场景分割

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995701

A. Costea, S. Nedevschi

{"title":"Traffic scene segmentation based on boosting over multimodal low, intermediate and high order multi-range channel features","authors":"A. Costea, S. Nedevschi","doi":"10.1109/IVS.2017.7995701","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995701","url":null,"abstract":"In this paper we introduce a novel multimodal boosting based solution for semantic segmentation of traffic scenarios. Local structure and context are captured from both monocular color and depth modalities in the form of image channels. We define multiple channel types at three different levels: low, intermediate and high order channels. The low order channels are computed using a multimodal multiresolution filtering scheme and capture structure and color information from lower receptive fields. For the intermediate order channels, we employ deep convolutional channels that are able to capture more complex structures, having a larger receptive field. The high order channels are scale invariant channels that consist of spatial, geometric and semantic channels. These channels are enhanced by additional pyramidal context channels, capturing context at multiple levels. The semantic segmentation is achieved by a boosting based classification scheme over superpixels using multi-range channel features and pyramidal context features. A pre-segmentation is used to generate semantic channels as input for more powerful final segmentation. The final segmentation is refined using a superpixel-level dense conditional random field. The proposed solution is evaluated on the Cityscapes segmentation benchmark and achieves competitive results at low computational costs. It is the first boosting based solution that is able to keep up with the performance of deep learning based approaches.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132366393","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: 6

Pedestrian tracking using Velodyne data — Stochastic optimization for extended object tracking 使用Velodyne数据的行人跟踪-扩展对象跟踪的随机优化

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995696

Karl Granström, Stephan Renter, M. Fatemi, L. Svensson

引用次数: 41

A hybrid bundle adjustment/pose-graph approach to VSLAM/GPS fusion for low-capacity platforms 低容量平台VSLAM/GPS融合的混合束平差/姿态图方法

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995957

Achkan Salehi, V. Gay-Bellile, S. Bourgeois, F. Chausse

{"title":"A hybrid bundle adjustment/pose-graph approach to VSLAM/GPS fusion for low-capacity platforms","authors":"Achkan Salehi, V. Gay-Bellile, S. Bourgeois, F. Chausse","doi":"10.1109/IVS.2017.7995957","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995957","url":null,"abstract":"We focus on the real-time fusion of monocular visual SLAM with GPS data in order to obtain city-scale, georeferenced pose estimations and reconstructions. Recently, GPS/VSLAM fusion through constrained local key-frame based Bundle Adjustment (BA) using Barrier Term Optimization (BTO) has proven to be (to the best of our knowledge) the most robust and accurate method. However, this approach requires a higher number of cameras to be considered in the optimization: in practice, more than 30 cameras are necessary, while a typical vision-only BA can succeed with as few as 10 cameras. This problem dimensionality makes the method unsuitable for autonomous embedded platforms of low computational capacity (e.g. MAVs). In this paper, we present a hybrid constrained BA/pose-graph approach using BTO, which is motivated by theoretical observations about covariance changes as a function of the gauge. We show that our method has desirable properties that allows its successful use in a BTO context, and present two different formulations. The experimental validation of our method shows that both our formulations reduce the computational cost in comparison with constrained BA using BTO, without any significant loss of precision. In particular, our first formulation yields a 60% reduction in execution time.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116622009","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

Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field 基于时空条件随机场的地面估计和点云分割

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995861

Lukas Rummelhard, Anshul K. Paigwar, Amaury Nègre, C. Laugier

{"title":"Ground estimation and point cloud segmentation using SpatioTemporal Conditional Random Field","authors":"Lukas Rummelhard, Anshul K. Paigwar, Amaury Nègre, C. Laugier","doi":"10.1109/IVS.2017.7995861","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995861","url":null,"abstract":"Whether it be to feed data for an object detection-and-tracking system or to generate proper occupancy grids, 3D point cloud extraction of the ground and data classification are critical processing tasks, on their efficiency can drastically depend the whole perception chain. Flat-ground assumption or form recognition in point clouds can either lead to systematic error, or massive calculations. This paper describes an adaptive method for ground labeling in 3D Point clouds, based on a local ground elevation estimation. The system proposes to model the ground as a Spatio-Temporal Conditional Random Field (STCRF). Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). Ground elevation parameters are estimated in parallel in each node, using an interconnected Expectation Maximization (EM) algorithm variant. The approach, designed to target high-speed vehicle constraints and performs efficiently with highly-dense (Velodyne-64) and sparser (Ibeo-Lux) 3D point clouds, has been implemented and deployed on experimental vehicle and platforms, and are currently tested on embedded systems (Nvidia Jetson TX1, TK1). The experiments on real road data, in various situations (city, countryside, mountain roads,…), show promising results.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114884262","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: 41

End-to-end learning for lane keeping of self-driving cars 自动驾驶汽车的端到端车道保持学习

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995975

Zhilu Chen, Xinming Huang

引用次数: 175

GNSS-shortages-resistant and self-adaptive rear axle kinematic parameter estimator (SA-RAKPE) 抗gnss缺陷自适应后桥运动参数估计器(SA-RAKPE)

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995760

Alexander Brunker, T. Wohlgemuth, Michael Frey, F. Gauterin

{"title":"GNSS-shortages-resistant and self-adaptive rear axle kinematic parameter estimator (SA-RAKPE)","authors":"Alexander Brunker, T. Wohlgemuth, Michael Frey, F. Gauterin","doi":"10.1109/IVS.2017.7995760","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995760","url":null,"abstract":"This paper investigates the improvements from an intelligent self-adaptive modification to a Global Navigatior Satellite System (GNSS)-Based Rear Axle Kinematic Parametei Estimator (SA-RAKPE) for an automatic-driving-system in a passenger vehicle. The required highly accurate dead-reckoning localization can be achieved by a well-calibrated kinematic odometry model. For this purpose, the presented Extended Kalman filter approach combines a Differential-Velocity system model and a GNSS measurement model. Subsequently the intelligent self-adaptive modifications are introduced to allow the SA-RAKPE to work even under difficult conditions The self-adaptive modifications include a GNSS-Delay-Finder Module that calculates variable delays of the signals used in complex vehicle architectures. The newly developed SA-RAPKE deals with changes in the system and measurement mode accuracies and even works during interruptions caused by GNSS-shortages. To do this, it changes the update equation and fills the interruptions with virtual parameter measurement to avoid estimation inaccuracies from observability loss and even to store the level of learned parameters. After passing the GNSS-shortages, the filter compensates the error in the system model depending on the length of the GNSS-shortage This makes it possible to continue the parameter learning while passing a great number of bad condition passages. This newly developed self-adaptive filter learns the true axle parameter faster than a restartable filter. The results show that despite numerous high-rise zones, tunnels and bridges, outstanding performance and a short learning phase ensue, especially in urban areas.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130103966","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: 17

Path planning for autonomous vehicles using model predictive control 基于模型预测控制的自动驾驶汽车路径规划

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995716

Chang Liu, Seungho Lee, S. Varnhagen, H. E. Tseng

{"title":"Path planning for autonomous vehicles using model predictive control","authors":"Chang Liu, Seungho Lee, S. Varnhagen, H. E. Tseng","doi":"10.1109/IVS.2017.7995716","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995716","url":null,"abstract":"Path planning for autonomous vehicles in dynamic environments is an important but challenging problem, due to the constraints of vehicle dynamics and existence of surrounding vehicles. Typical trajectories of vehicles involve different modes of maneuvers, including lane keeping, lane change, ramp merging, and intersection crossing. There exist prior arts using the rule-based high-level decision making approaches to decide the mode switching. Instead of using explicit rules, we propose a unified path planning approach using Model Predictive Control (MPC), which automatically decides the mode of maneuvers. To ensure safety, we model surrounding vehicles as polygons and develop a type of constraints in MPC to enforce the collision avoidance between the ego vehicle and surrounding vehicles. To achieve comfortable and natural maneuvers, we include a lane-associated potential field in the objective function of the MPC. We have simulated the proposed method in different test scenarios and the results demonstrate the effectiveness of the proposed approach in automatically generating reasonable maneuvers while guaranteeing the safety of the autonomous vehicle.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116929598","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: 128