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2017 IEEE Intelligent Vehicles Symposium (IV)最新文献

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Navigation-orientated natural spoken language understanding for intelligent vehicle dialogue 面向导航的自然口语理解智能车辆对话
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995777
Yang Zheng, Yongkang Liu, J. Hansen
{"title":"Navigation-orientated natural spoken language understanding for intelligent vehicle dialogue","authors":"Yang Zheng, Yongkang Liu, J. Hansen","doi":"10.1109/IVS.2017.7995777","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995777","url":null,"abstract":"Voice-based human-machine interfaces are becoming a key feature for next generation intelligent vehicles. For the navigation dialogue systems, it is desired to understand a driver's spoken language in a natural way. This study proposes a two-stage framework, which first converts the audio streams into text sentences through Automatic Speech Recognition (ASR), followed by Natural Language Processing (NLP) to retrieve the navigation-associated information. The NLP stage is based on a Deep Neural Network (DNN) framework, which contains sentence-level sentiment analysis and word/phrase-level context extraction. Experiments are conducted using the CU-Move in-vehicle speech corpus. Results indicate that the DNN architecture is effective for navigation dialog language understanding, whereas the NLP performances are affected by ASR errors. Overall, it is expected that the proposed RNN-based NLP approach, with the corresponding reduced vocabulary designed for navigation-oriented tasks, will benefit the development of advanced intelligent vehicle human-machine interfaces.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"14 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":"123036734","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: 22
Vehicle speed prediction using a cooperative method of fuzzy Markov model and auto-regressive model 基于模糊马尔可夫模型和自回归模型的车速预测方法
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995827
Junbo Jing, Dimitar Filev, A. Kurt, E. Ozatay, J. Michelini, Ü. Özgüner
{"title":"Vehicle speed prediction using a cooperative method of fuzzy Markov model and auto-regressive model","authors":"Junbo Jing, Dimitar Filev, A. Kurt, E. Ozatay, J. Michelini, Ü. Özgüner","doi":"10.1109/IVS.2017.7995827","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995827","url":null,"abstract":"Vehicle speed prediction can benefit a wide range of vehicle control designs, especially for fuel economy applications. This paper shows a computationally light vehicle short term speed predictor designed for on-board implementation, using minimal information of speed measurement only. The predictor generalizes historical speed data's underlying pattern and predicts from probability aspect. One novelty of the method is the usage of fuzzy modeling to eliminate the resolution limitation in vehicle acceleration state definition, classification, and prediction. The method uses Auto-regressive (AR) model to capture vehicle speed data's short term dynamics, and classifies the data into multiple acceleration states by fuzzy membership. In the prediction process, acceleration measurements are mapped to the Markov states by fuzzy encoding, and future acceleration states are predicted by Markov transition. Deterministic speed prediction is calculated from the trained AR models, which are selected by fuzzy state membership similarity. The developed predictor is tested with a vehicle's real urban driving data, and the effectiveness of the incorporated techniques is verified by a comparison study.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"22 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":"114508477","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: 33
A model predictive-based approach for longitudinal control in autonomous driving with lateral interruptions 基于模型预测的横向中断自动驾驶纵向控制方法
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995745
Kai Liu, Jian-wei Gong, A. Kurt, Huiyan Chen, Ü. Özgüner
{"title":"A model predictive-based approach for longitudinal control in autonomous driving with lateral interruptions","authors":"Kai Liu, Jian-wei Gong, A. Kurt, Huiyan Chen, Ü. Özgüner","doi":"10.1109/IVS.2017.7995745","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995745","url":null,"abstract":"The longitudinal control of an autonomous vehicle usually suffers from lateral interruptions, such as the cutting in/out of the lead vehicle, deteriorating its performance and even endangering driving safety. To address this problem, we present a model predictive-based approach for longitudinal control in autonomous driving by taking the lateral interruptions into account. First, a virtual lead vehicle scheme is introduced to predict the future behavior of the actual lead vehicle. By following the virtual lead vehicle rather than the actual lead vehicle, the control of the host vehicle is simplified to keep a proper following gap problem. Then, a strategic car-following gap (CFG) model, generated from highway naturalistic driving data, is employed to describe the safety hazard and the probability of cut-ins by other vehicles. A model predictive controller, incorporating the strategic CFG model as well as the acceleration and jerk limitations in the objective function, is designed for the longitudinal control of the host vehicle. Solving the optimal control problem can not only smooth the oscillation and overshoots caused by the lateral interruptions but also reduce the probability of cut-ins from the adjacent lanes. The proposed approach is simulated and validated through some predefined test scenarios in CarSim software.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"31 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":"114603617","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
On generalizing driver gaze zone estimation using convolutional neural networks 基于卷积神经网络的驾驶员注视区域估计泛化研究
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995822
Sourabh Vora, Akshay Rangesh, M. Trivedi
{"title":"On generalizing driver gaze zone estimation using convolutional neural networks","authors":"Sourabh Vora, Akshay Rangesh, M. Trivedi","doi":"10.1109/IVS.2017.7995822","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995822","url":null,"abstract":"The knowledge of driver distraction will be important for self driving cars in the near future to determine the handoff time to the driver. Driver's gaze direction has been previously shown as an important cue in understanding distraction. While there has been a significant improvement in personalized driver gaze zone estimation systems, a generalized gaze zone estimation system which is invariant to different subjects, perspective and scale is still lagging behind. We take a step towards the generalized system using a Convolutional Neural Network (CNN). For evaluating our system, we collect large naturalistic driving data of 11 drives, driven by 10 subjects in two different cars and label gaze zones for 47515 frames. We train our CNN on 7 subjects and test on the other 3 subjects. Our best performing model achieves an accuracy of 93.36% showing good generalization capability.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"258 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":"122141030","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: 66
Detection of valuable left-behind items in vehicle cabins 检测车内遗留的贵重物品
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995862
Toby Perrett, M. Mirmehdi, Eduardo Dias
{"title":"Detection of valuable left-behind items in vehicle cabins","authors":"Toby Perrett, M. Mirmehdi, Eduardo Dias","doi":"10.1109/IVS.2017.7995862","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995862","url":null,"abstract":"We propose a method for detecting valuable left-behind items in vehicle cabins which uses a single overhead camera. An additional sub-network is incorporated into the Faster R-CNN framework in order to allow it to estimate item value based on visual properties, as well as to perform detection. A loss function which contains a user-specified minimum-value threshold is introduced, which enables warnings to be given if a detected item is above this threshold. As a significant amount of real data is time consuming to collect on the scale necessary for (deep) learning-based methods, an ImageNet model is first retrained on synthetic data to adapt it to our environment, before training on some real data. The effectiveness of this detection and validation approach is demonstrated by integrating additional valuation subnetworks into two convolutional neural network detection architectures.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"1 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":"129804821","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
Exact inference and learning in hybrid Bayesian Networks for lane change intention classification 混合贝叶斯网络在变道意图分类中的精确推理与学习
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995927
A. Koenig, Tobias Rehder, S. Hohmann
{"title":"Exact inference and learning in hybrid Bayesian Networks for lane change intention classification","authors":"A. Koenig, Tobias Rehder, S. Hohmann","doi":"10.1109/IVS.2017.7995927","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995927","url":null,"abstract":"Determining the current intentions of other drivers is essential for correctly predicting or simulating their future actions. Especially unpredicted lane changes can result in very uncomfortable or even dangerous braking maneuvers for succeeding vehicles. Bayesian Networks (BN) allow for a physically motivated probabilistic representation of features influencing driver intentions. While features often take continuous values, e.g. velocity and distance, maneuver intentions are discrete, which results in hybrid BN. For efficient and exact inference, we implement an approach for hybrid nets into the original Bayes Net Toolbox. Furthermore, we extend the approach with a learning component to train a BN with simulated traffic data. Finally, we compare the classification performance for lane changes with a Deep Neural Network (DNN) classifier.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"15 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":"128285886","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
Mapping and localization using surround view 使用环绕视图映射和定位
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995869
Marc Sons, M. Lauer, C. G. Keller, C. Stiller
{"title":"Mapping and localization using surround view","authors":"Marc Sons, M. Lauer, C. G. Keller, C. Stiller","doi":"10.1109/IVS.2017.7995869","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995869","url":null,"abstract":"Intelligent vehicles heavily rely on robust and accurate self-localization. Global navigation satellite systems (GNSS) are not reliable in urban environments due to multipath and shadowing effects. Vision-based localization offers a promising alternative. We present a high-precision six degrees of freedom self-localization method using multiple cameras covering the surrounding environment. First, a point feature map is created using images from a previous pass of the area to map. Thereafter, the map is used for high-precision localization in real-time. While localization, a rough prior estimate of the current pose is used to shrink the search space for feature matching by projecting mapped landmarks into current images. Then, stored observations of the projected landmarks are matched to actual observations and the egopose is estimated by back-projection error minimization. Thereby, our map structure provides mapped landmarks efficiently towards localization with multiple cameras. In real-world experiments we show that our approach provides reliable localization results while passing the mapped area in arbitrary orientation.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"8 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":"129159574","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: 22
Cross-regional study on driver response behavior patterns and system acceptance with triggered forward collision warning 前向碰撞预警触发下驾驶员响应行为模式及系统接受度的跨区域研究
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995778
Yuan Liao, Lian Duan, M. Wang, Fang Chen
{"title":"Cross-regional study on driver response behavior patterns and system acceptance with triggered forward collision warning","authors":"Yuan Liao, Lian Duan, M. Wang, Fang Chen","doi":"10.1109/IVS.2017.7995778","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995778","url":null,"abstract":"Understanding complex behavior patterns in response to triggered forward collision warning system benefits localized user experience design, especially in safety critical scenarios from a cross-regional perspective. This paper studies driver response behavior patterns towards two alarm timings, in car-following scenarios with different traffic density. Data from 32 participants in China and 30 participants in Sweden were collected using a driving simulator. Differences were observed between China group and Sweden group regarding response behavior patterns, system acceptance and effectiveness. Seen from obtained results, Chinese drivers were found to steer more frequently than Swedish drivers in response to triggered alarm no matter what alarm timing or traffic density there were. Chinese drivers preferred later alarm timing than Swedish drivers. To better design regional-adaptive human machine interaction of forward collision warning system; some suggestions were produced based on obtained results.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"68 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":"126903876","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: 3
Cyclists' starting behavior at intersections 骑自行车的人在十字路口的起始行为
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995856
Andreas Hubert, Stefan Zernetsch, Konrad Doll, B. Sick
{"title":"Cyclists' starting behavior at intersections","authors":"Andreas Hubert, Stefan Zernetsch, Konrad Doll, B. Sick","doi":"10.1109/IVS.2017.7995856","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995856","url":null,"abstract":"In the context of highly automated driving an important aspect is the understanding of Vulnerable Road Users' behavior. In this article we concentrate on starting cyclists at an urban intersection and investigate 104 trajectories of uninstructed cyclists' heads, feet, and bikes. A detailed analysis shows that on average cyclists' heads start moving 0.33 s earlier than the bike. Even 0.31 s before that, about 29 % of the cyclists start with an arm movement. Additionally, we observe two different starting motion patterns. One group accelerates by pushing off the ground with a foot and the other only applies accelerating force to a pedal. Separating the data by different slopes of the road and motion patterns, the analysis shows that motion patterns have more influence on the starting behavior than the slope at the investigated intersection.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"85 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":"127028948","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
A new control architecture for MuCAR MuCAR的新控制体系结构
2017 IEEE Intelligent Vehicles Symposium (IV) Pub Date : 2017-06-01 DOI: 10.1109/IVS.2017.7995976
Benjamin C. Heinrich, T. Luettel, Hans-Joachim Wünsche
{"title":"A new control architecture for MuCAR","authors":"Benjamin C. Heinrich, T. Luettel, Hans-Joachim Wünsche","doi":"10.1109/IVS.2017.7995976","DOIUrl":"https://doi.org/10.1109/IVS.2017.7995976","url":null,"abstract":"The Munich Cognitive Autonomous Robot Car 3rd Generation (MuCAR-3) has won several international achievements in the past. Recently, the system's control architecture (meaning the interplay between perception, planning and control) was overhauled. Our goals were to simplify the interaction between modules as well as to meet higher requirements for both smoothness and precision. The decoupling of modules helps with tackling more challenging scenarios and facilitates the development of each module. Since state machines struggle with scalability, its interactions with other modules were minimized. We now use a generalized planning layer rather than so-called maneuvers. This paper aims at showcasing the difference between our previous and current architecture. We focus on the improvements that were achieved even for very simple scenarios — in this case off-road platooning. Using the same control algorithms, we achieve both improvements in smoothness and precision, two classically orthogonal goals. Tests were conducted in simulation and verified with MuCAR-3 on our test site.","PeriodicalId":143367,"journal":{"name":"2017 IEEE Intelligent Vehicles Symposium (IV)","volume":"3 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":"123190744","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: 3
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