2017 IEEE Intelligent Vehicles Symposium (IV)最新文献_第3页

Vehicle positioning with the integration of scene understanding and 3D map in urban environment 城市环境中场景理解与三维地图相结合的车辆定位

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995700

Jiali Bao, Yanlei Gu, S. Kamijo

{"title":"Vehicle positioning with the integration of scene understanding and 3D map in urban environment","authors":"Jiali Bao, Yanlei Gu, S. Kamijo","doi":"10.1109/IVS.2017.7995700","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995700","url":null,"abstract":"Accurate self-localization is a critical problem in autonomous driving system. An autonomous vehicle requires sub-meter level positioning to make motion planning. However, in urban scenarios, the common Global Navigation Satellite System (GNSS) localization suffers from various difficulties as multipath and Non-Line-Of-Sight (NLOS). The Stereo visual odometry proves to be capable of localizing the vehicle relatively by tracking the ego motion of vehicle from stereo image pairs, but with cumulative error. 3D Map is an effective tool to reduce the cumulative positioning error. In this paper, we propose to realize scene understanding from stereo camera, and further utilize city mode map including 3D building and 2D road information to improve the visual odometry. In our proposal, stereo camera is applied to generate visual odometry and reconstruct the building scene. The accumulated building scenes form local building map. We integrate the local building map and Normal Distribution Transform (NDT) map generated from 3D building map in particle filter. Lane detection result helps to rectify the inner lane positioning error and keep lane with the aid of 2D road map. We conducted a series of experiments in Hitotsubashi area of Tokyo city where locates a lot of tall buildings. The result of experiments indicates that the accumulated error of visual odometry can be corrected by the proposed method and sub-meter accuracy localization is achieved.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"103 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134393427","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: 7

Verifying the safety of lane change maneuvers of self-driving vehicles based on formalized traffic rules 基于形式化交通规则的自动驾驶车辆变道操作安全性验证

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995918

Christian Pek, P. Zahn, M. Althoff

引用次数: 58

A computational framework for driver's visual attention using a fully convolutional architecture 使用全卷积架构的驾驶员视觉注意力计算框架

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995828

Ashish Tawari, Byeongkeun Kang

引用次数: 35

Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles 考虑周围车辆相互作用和不确定预测的自动驾驶决策

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995949

Constantin Hubmann, Marvin Becker, Daniel Althoff, David Lenz, C. Stiller

{"title":"Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles","authors":"Constantin Hubmann, Marvin Becker, Daniel Althoff, David Lenz, C. Stiller","doi":"10.1109/IVS.2017.7995949","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995949","url":null,"abstract":"Autonomous driving requires decision making in dynamic and uncertain environments. The uncertainty from the prediction originates from the noisy sensor data and from the fact that the intention of human drivers cannot be directly measured. This problem is formulated as a partially observable Markov decision process (POMDP) with the intention of the other vehicles as hidden variables. The solution of the POMDP is a policy determining the optimal acceleration of the ego vehicle along a preplanned path. Therefore, the policy is optimized for the most likely future scenarios resulting from an interactive, probabilistic motion model for the other vehicles. Considering possible future measurements of the surroundings allows the autonomous car to incorporate the estimated change in future prediction accuracy in the optimal policy. A compact representation allows a low-dimensional state-space so that the problem can be solved online for varying road layouts and number of other vehicles. This is done with a point-based solver in an anytime fashion on a continuous state-space. We show the results with simulations for the crossing of complex (unsignalized) intersections. Our approach performs nearly as good as with full prior information about the intentions of the other vehicles and clearly outperforms reactive approaches.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"52 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":"114732161","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: 150

Speed profile planning in dynamic environments via temporal optimization 动态环境下基于时间优化的速度剖面规划

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995713

Changliu Liu, W. Zhan, M. Tomizuka

引用次数: 44

A machine learning approach for personalized autonomous lane change initiation and control 个性化自动变道启动与控制的机器学习方法

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995936

Charlott Vallon, Ziya Ercan, Ashwin Carvalho, F. Borrelli

{"title":"A machine learning approach for personalized autonomous lane change initiation and control","authors":"Charlott Vallon, Ziya Ercan, Ashwin Carvalho, F. Borrelli","doi":"10.1109/IVS.2017.7995936","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995936","url":null,"abstract":"We study an algorithm that allows a vehicle to autonomously change lanes in a safe but personalized fashion without the driver's explicit initiation (e.g. activating the turn signals). Lane change initiation in autonomous driving is typically based on subjective rules, functions of the positions and relative velocities of surrounding vehicles. This approach is often arbitrary, and not easily adapted to the driving style preferences of an individual driver. Here we propose a data-driven modeling approach to capture the lane change decision behavior of human drivers. We collect data with a test vehicle in typical lane change situations and train classifiers to predict the instant of lane change initiation with respect to the preferences of a particular driver. We integrate this decision logic into a model predictive control (MPC) framework to create a more personalized autonomous lane change experience that satisfies safety and comfort constraints. We show the ability of the decision logic to reproduce and differentiate between two lane changing styles, and demonstrate the safety and effectiveness of the control framework through simulations.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"101 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":"133186208","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: 70

Automated driving by monocular camera using deep mixture of experts 使用深度混合专家的单目相机自动驾驶

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995709

V. John, S. Mita, Hossein Tehrani Niknejad, Kazuhisa Ishimaru

{"title":"Automated driving by monocular camera using deep mixture of experts","authors":"V. John, S. Mita, Hossein Tehrani Niknejad, Kazuhisa Ishimaru","doi":"10.1109/IVS.2017.7995709","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995709","url":null,"abstract":"In this paper, we propose a real-time vision-based filtering algorithm for steering angle estimation in autonomous driving. A novel scene-based particle filtering algorithm is used to estimate and track the steering angle using images obtained from a monocular camera. Highly accurate proposal distributions and likelihood are modeled for the second order particle filter, at the scene-level, using deep learning. For every road scene, an individual proposal distribution and likelihood model is learnt for the corresponding particle filter. The proposal distribution is modeled using a novel long short term memory network-mixture-of-expert-based regression framework. To facilitate the learning of highly accurate proposal distributions, each road scene is partitioned into straight driving, left turning and right turning sub-partitions. Subsequently, each expert in the regression framework accurately model the expert driver's behavior within a specific partition of the given road scene. Owing to the accuracy of the modelled proposal distributions, the steering angle is robustly tracked, even with a limited number of sampled particles. The sampled particles are assigned importance weights using a deep learning-based likelihood. The likelihood is modeled with a convolutional neural network and extra trees-based regression framework, which predicts the steering angle for a given image. We validate our proposed algorithm using multiple sequences. We perform a detailed parameter analysis and a comparative analysis of our proposed algorithm with different baseline algorithms. Experimental results show that the proposed algorithm can robustly track the steering angles with few particles in real-time even for challenging scenes.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"25 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":"125120661","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

Refine pedestrian detections by referring to features in different ways 通过不同的方式参考特征来改进行人检测

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995754

Jaemyung Lee, Sihaeng Lee, Youngdong Kim, Janghyeon Lee, Junmo Kim

引用次数: 0

Intervention minimized semi-autonomous control using decoupled model predictive control 采用解耦模型预测控制的干预最小化半自主控制

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995787

Hayoung Kim, Jeongmin Cho, Dongchan Kim, K. Huh

引用次数: 13

Scale optimization for full-image-CNN vehicle detection 全图像- cnn车辆检测的尺度优化

2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-11 DOI: 10.1109/IVS.2017.7995812

Yang Gao, Shouyan Guo, Kai-Wei Huang, Jiaxin Chen, Q. Gong, Yang Zou, Tongyao Bai, G. Overett

{"title":"Scale optimization for full-image-CNN vehicle detection","authors":"Yang Gao, Shouyan Guo, Kai-Wei Huang, Jiaxin Chen, Q. Gong, Yang Zou, Tongyao Bai, G. Overett","doi":"10.1109/IVS.2017.7995812","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995812","url":null,"abstract":"Many state-of-the-art general object detection methods make use of shared full-image convolutional features (as in Faster R-CNN). This achieves a reasonable test-phase computation time while enjoys the discriminative power provided by large Convolutional Neural Network (CNN) models. Such designs excel on benchmarks1 which contain natural images but which have very unnatural distributions, i.e. they have an unnaturally high-frequency of the target classes and a bias towards a “friendly” or “dominant” object scale. In this paper we present further study of the use and adaptation of the Faster R-CNN object detection method for datasets presenting natural scale distribution and unbiased real-world object frequency. In particular, we show that better alignment of the detector scale sensitivity to the extant distribution improves vehicle detection performance. We do this by modifying both the selection of Region Proposals, and through using more scale-appropriate full-image convolution features within the CNN model. By selecting better scales in the region proposal input and by combining feature maps through careful design of the convolutional neural network, we improve performance on smaller objects. We significantly increase detection AP for the KITTI dataset car class from 76.3% on our baseline Faster R-CNN detector to 83.6% in our improved detector.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"41 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":"122660204","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: 27