Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940461
Bernhard Kloiber, T. Strang, Matthias Röckl, F. Müller
ETSI ITS-G5 is the current vehicle-to-vehicle communication technology in Europe, which will be standardized by ETSI TC ITS1. It is based on IEEE 802.11p and therefore uses a CSMA/CA scheme for Media Access Control (MAC). In this paper we analyze the performance of Cooperative Awareness Message (CAM) based safety applications using the ETSI ITS-G5 MAC technology in a challenging scenario with respect to MAC issues: A suitable freeway segment with 6 lanes in each direction. The freeway scenario is thoroughly modeled and implemented in the well known ns-3 simulation environment. Based on this model, the paper shows the performance of CAM based safety applications under MAC challenging conditions. We provide a set of simulation results resting upon a particular performance metric which incorporates the key requirements of safety applications. Finally we analyze two concrete example scenarios to determine how reliable CAM based safety applications are in high dense traffic scenarios with respect to MAC issues.
ETSI ITS-G5是欧洲目前的车对车通信技术，将由ETSI TC ITS1进行标准化。它基于IEEE 802.11p，因此使用CSMA/CA方案进行媒体访问控制(MAC)。在本文中，我们使用ETSI ITS-G5 MAC技术在一个具有挑战性的场景中分析了基于协同感知消息(CAM)的安全应用程序的性能，该场景涉及MAC问题:一个合适的高速公路路段，每个方向有6个车道。高速公路场景在著名的ns-3仿真环境中进行了彻底的建模和实现。基于该模型，本文展示了基于CAM的安全应用程序在MAC挑战条件下的性能。我们提供了一组基于特定性能指标的模拟结果，该指标包含了安全应用的关键要求。最后，我们分析了两个具体的示例场景，以确定基于CAM的安全应用程序在高密度交通场景中对于MAC问题的可靠性。
{"title":"Performance of CAM based safety applications using ITS-G5A MAC in high dense scenarios","authors":"Bernhard Kloiber, T. Strang, Matthias Röckl, F. Müller","doi":"10.1109/IVS.2011.5940461","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940461","url":null,"abstract":"ETSI ITS-G5 is the current vehicle-to-vehicle communication technology in Europe, which will be standardized by ETSI TC ITS1. It is based on IEEE 802.11p and therefore uses a CSMA/CA scheme for Media Access Control (MAC). In this paper we analyze the performance of Cooperative Awareness Message (CAM) based safety applications using the ETSI ITS-G5 MAC technology in a challenging scenario with respect to MAC issues: A suitable freeway segment with 6 lanes in each direction. The freeway scenario is thoroughly modeled and implemented in the well known ns-3 simulation environment. Based on this model, the paper shows the performance of CAM based safety applications under MAC challenging conditions. We provide a set of simulation results resting upon a particular performance metric which incorporates the key requirements of safety applications. Finally we analyze two concrete example scenarios to determine how reliable CAM based safety applications are in high dense traffic scenarios with respect to MAC issues.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128724352","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940422
Fabian Diewald, J. Klappstein, Frederik Sarholz, J. Dickmann, K. Dietmayer
Automotive radar sensors are commonly used for providing environment information required by driver assistance systems. Although they show a good performance in measuring the distance and the speed of other objects even under poor weather conditions, they suffer from the shortcoming of a missing resolution in elevation. Consequently bridges crossing the lane of the ego vehicle may look similar to stationary obstacles. This contribution shows a bridge identification algorithm based on the interference pattern resulting from the multipath propagation of the radar wave. During the approaching to bridges or stationary obstacles, the pattern leads to a variation in the backscattered power from the object. This variation can be used to make statements about the object height position above the road. Results calculated from real scanning radar sensor data show the usability in real traffic scenarios.
{"title":"Radar-interference-based bridge identification for collision avoidance systems","authors":"Fabian Diewald, J. Klappstein, Frederik Sarholz, J. Dickmann, K. Dietmayer","doi":"10.1109/IVS.2011.5940422","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940422","url":null,"abstract":"Automotive radar sensors are commonly used for providing environment information required by driver assistance systems. Although they show a good performance in measuring the distance and the speed of other objects even under poor weather conditions, they suffer from the shortcoming of a missing resolution in elevation. Consequently bridges crossing the lane of the ego vehicle may look similar to stationary obstacles. This contribution shows a bridge identification algorithm based on the interference pattern resulting from the multipath propagation of the radar wave. During the approaching to bridges or stationary obstacles, the pattern leads to a variation in the backscattered power from the object. This variation can be used to make statements about the object height position above the road. Results calculated from real scanning radar sensor data show the usability in real traffic scenarios.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117010731","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940402
S. Durekovic, N. Smith
This document describes possible architectures of a map-supported Advanced Driver Assistant Systems. It also explains the advantages of a standardized Application Programming Interfaces and Protocol with special emphasis on the ADASIS v2 Protocol and API standard.
{"title":"Architectures of Map-Supported ADAS","authors":"S. Durekovic, N. Smith","doi":"10.1109/IVS.2011.5940402","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940402","url":null,"abstract":"This document describes possible architectures of a map-supported Advanced Driver Assistant Systems. It also explains the advantages of a standardized Application Programming Interfaces and Protocol with special emphasis on the ADASIS v2 Protocol and API standard.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"37 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116786487","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940467
Keisuke Doman, Daisuke Deguchi, Tomokazu Takahashi, Y. Mekada, I. Ide, H. Murase, Y. Tamatsu
We propose a visibility estimation method for traffic signs considering temporal environmental changes, as a part of work for the realization of nuisance-free driver assistance systems. Recently, the number of driver assistance systems in a vehicle is increasing. Accordingly, it is becoming important to sort out appropriate information provided from them, because providing too much information may cause driver distraction. To solve such a problem, we focus on a visibility estimation method for controlling the information according to the visibility of a traffic sign. The proposed method sequentially captures a traffic sign by an in-vehicle camera, and estimates its accumulative visibility by integrating a series of instantaneous visibility. By this way, even if the environmental conditions may change temporally and complicatedly, we can still accurately estimate the visibility that the driver perceives in an actual traffic scene. We also investigate the performance of the proposed method and show its effectiveness.
{"title":"Estimation of traffic sign visibility considering temporal environmental changes for smart driver assistance","authors":"Keisuke Doman, Daisuke Deguchi, Tomokazu Takahashi, Y. Mekada, I. Ide, H. Murase, Y. Tamatsu","doi":"10.1109/IVS.2011.5940467","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940467","url":null,"abstract":"We propose a visibility estimation method for traffic signs considering temporal environmental changes, as a part of work for the realization of nuisance-free driver assistance systems. Recently, the number of driver assistance systems in a vehicle is increasing. Accordingly, it is becoming important to sort out appropriate information provided from them, because providing too much information may cause driver distraction. To solve such a problem, we focus on a visibility estimation method for controlling the information according to the visibility of a traffic sign. The proposed method sequentially captures a traffic sign by an in-vehicle camera, and estimates its accumulative visibility by integrating a series of instantaneous visibility. By this way, even if the environmental conditions may change temporally and complicatedly, we can still accurately estimate the visibility that the driver perceives in an actual traffic scene. We also investigate the performance of the proposed method and show its effectiveness.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115179883","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940538
B. Morris, A. Doshi, M. Trivedi
Automobiles are quickly becoming more complex as new sensors and support systems are being added to improve safety and comfort. The next generation of intelligent driver assistance systems will need to utilize this wide array of sensors to fully understand the driving context and situation. Effective interaction requires these systems to examine the intentions, desires, and needs of the driver for preemptive actions which can help prepare for or avoid dangerous situations. This manuscript develops a real-time on-road prediction system able to detect a driver's intention to change lanes seconds before it occurs. In-depth analysis highlights the challenges when moving intent prediction from the laboratory to the road and provides detailed characterization of on-road performance.
{"title":"Lane change intent prediction for driver assistance: On-road design and evaluation","authors":"B. Morris, A. Doshi, M. Trivedi","doi":"10.1109/IVS.2011.5940538","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940538","url":null,"abstract":"Automobiles are quickly becoming more complex as new sensors and support systems are being added to improve safety and comfort. The next generation of intelligent driver assistance systems will need to utilize this wide array of sensors to fully understand the driving context and situation. Effective interaction requires these systems to examine the intentions, desires, and needs of the driver for preemptive actions which can help prepare for or avoid dangerous situations. This manuscript develops a real-time on-road prediction system able to detect a driver's intention to change lanes seconds before it occurs. In-depth analysis highlights the challenges when moving intent prediction from the laboratory to the road and provides detailed characterization of on-road performance.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114713165","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940557
T. Ruland, T. Pajdla, Lars Krüger
This paper introduces simultaneous global optimization of both camera orientation and vehicle wheel circumference without requiring any information about the translations in the system. The main contribution are new objective function bounds to integrate this problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator for both components of the problem with respect to a cost function based on reprojection errors. The algorithm operates directly on image measurements and does not depend on any structure and motion preprocessing to estimate camera poses. The complete system is implemented and validated on both synthetic and real automotive datasets.
{"title":"Global optimization of extended hand-eye calibration","authors":"T. Ruland, T. Pajdla, Lars Krüger","doi":"10.1109/IVS.2011.5940557","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940557","url":null,"abstract":"This paper introduces simultaneous global optimization of both camera orientation and vehicle wheel circumference without requiring any information about the translations in the system. The main contribution are new objective function bounds to integrate this problem into a branch-and-bound parameter space search. The presented method constitutes the first guaranteed globally optimal estimator for both components of the problem with respect to a cost function based on reprojection errors. The algorithm operates directly on image measurements and does not depend on any structure and motion preprocessing to estimate camera poses. The complete system is implemented and validated on both synthetic and real automotive datasets.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127642080","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940511
A. Bazzi, B. Masini
Real time information to vehicular users is proposing new challenging questions to which wireless systems designers are called to answer. Nowadays many vehicles are already equipped with devices able to connect to cellular networks, and to transmit and receive in real time traffic information through vehicle-to-infrastructure (V2I) communication. Focusing on the uplink transmission of real time measurements, this is leading to high costs in terms of network load and billing. In this work we discuss the opportunity to take advantage of vehicle-to-vehicle (V2V) in addition to V2I communications to reduce the amount of data to be transmitted from vehicles to a remote control center, and thus also to reduce the resulting costs for transmissions over the cellular networks. Having in mind to allow a first understanding of the achievable advantages, we propose a simple mathematical model through which we discuss how many vehicles are necessary to guarantee an useful V2V communication and which are the advantages in terms of network load and, consequently, costs reductions for the V2I network.
{"title":"Taking advantage of V2V communications for traffic management","authors":"A. Bazzi, B. Masini","doi":"10.1109/IVS.2011.5940511","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940511","url":null,"abstract":"Real time information to vehicular users is proposing new challenging questions to which wireless systems designers are called to answer. Nowadays many vehicles are already equipped with devices able to connect to cellular networks, and to transmit and receive in real time traffic information through vehicle-to-infrastructure (V2I) communication. Focusing on the uplink transmission of real time measurements, this is leading to high costs in terms of network load and billing. In this work we discuss the opportunity to take advantage of vehicle-to-vehicle (V2V) in addition to V2I communications to reduce the amount of data to be transmitted from vehicles to a remote control center, and thus also to reduce the resulting costs for transmissions over the cellular networks. Having in mind to allow a first understanding of the achievable advantages, we propose a simple mathematical model through which we discuss how many vehicles are necessary to guarantee an useful V2V communication and which are the advantages in terms of network load and, consequently, costs reductions for the V2I network.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128049097","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940432
Kunihiro Goto, K. Kidono, Y. Kimura, T. Naito
This paper proposes a pedestrian detection and direction estimation method by the cascade approach with multiclassifiers using the Feature Interaction Descriptor (FIND). FIND describes the high-level properties of an object's appearance by computing pair-wise interactions of adjacent regionlevel features. To perform efficient and accurate detection using FIND, we employ the cascade approach with multiclassifiers specialized in both the direction of a pedestrian and the distance of the pedestrian from a camera. Using this framework, the developed system can improve the detection performance and provide information of the direction of a pedestrian simultaneously. The experimental results show that superior detection performance and direction estimation results were obtained by our method.
{"title":"Pedestrian detection and direction estimation by cascade detector with multi-classifiers utilizing feature interaction descriptor","authors":"Kunihiro Goto, K. Kidono, Y. Kimura, T. Naito","doi":"10.1109/IVS.2011.5940432","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940432","url":null,"abstract":"This paper proposes a pedestrian detection and direction estimation method by the cascade approach with multiclassifiers using the Feature Interaction Descriptor (FIND). FIND describes the high-level properties of an object's appearance by computing pair-wise interactions of adjacent regionlevel features. To perform efficient and accurate detection using FIND, we employ the cascade approach with multiclassifiers specialized in both the direction of a pedestrian and the distance of the pedestrian from a camera. Using this framework, the developed system can improve the detection performance and provide information of the direction of a pedestrian simultaneously. The experimental results show that superior detection performance and direction estimation results were obtained by our method.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128114679","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940416
Tobias Kühnl, F. Kummert, J. Fritsch
In this paper a novel approach for road detection with a monocular camera is introduced. We propose a two step approach, combining a patch-based segmentation with additional boundary detection. We use Slow Feature Analysis (SFA) which leads to improved appearance descriptors for road and non-road parts on patch level. From the slow features a low order feature set is formed which is used together with color and Walsh Hadamard texture features to train a patch-based GentleBoost classifier. This allows extracting areas from the image that correspond to the road with a certain confidence. Typically the border regions between road and non-road have the highest classification error rates, because the appearance is hard to distinguish on the patch level. Therefore we propose a post-processing step with a specialized classifier applied to the boundary region of the image to improve the segmentation results. In order to evaluate the quality of road segmentation we propose an application-based quality measurement applying an inverse perspective mapping on the image to obtain a Birds Eye View (BEV). The advantage of this approach is that the important distant parts and boundaries of the road in the real world, which are only a low fraction in the perspective image, can be assessed in this metric measure significantly better than on the pixel level. In addition, we estimate the driving corridor width and boundary error, because for Advanced Driver Assistant Systems (ADAS) metric information is needed. For all evaluations in different road and weather conditions, our system shows an improved performance of the two step approach compared to the basic segmentation.
{"title":"Monocular road segmentation using slow feature analysis","authors":"Tobias Kühnl, F. Kummert, J. Fritsch","doi":"10.1109/IVS.2011.5940416","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940416","url":null,"abstract":"In this paper a novel approach for road detection with a monocular camera is introduced. We propose a two step approach, combining a patch-based segmentation with additional boundary detection. We use Slow Feature Analysis (SFA) which leads to improved appearance descriptors for road and non-road parts on patch level. From the slow features a low order feature set is formed which is used together with color and Walsh Hadamard texture features to train a patch-based GentleBoost classifier. This allows extracting areas from the image that correspond to the road with a certain confidence. Typically the border regions between road and non-road have the highest classification error rates, because the appearance is hard to distinguish on the patch level. Therefore we propose a post-processing step with a specialized classifier applied to the boundary region of the image to improve the segmentation results. In order to evaluate the quality of road segmentation we propose an application-based quality measurement applying an inverse perspective mapping on the image to obtain a Birds Eye View (BEV). The advantage of this approach is that the important distant parts and boundaries of the road in the real world, which are only a low fraction in the perspective image, can be assessed in this metric measure significantly better than on the pixel level. In addition, we estimate the driving corridor width and boundary error, because for Advanced Driver Assistant Systems (ADAS) metric information is needed. For all evaluations in different road and weather conditions, our system shows an improved performance of the two step approach compared to the basic segmentation.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127296993","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}
Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940441
O. Mazhelis
Predicting routes is a critical enabler for many new location-based applications and services, such as warning drivers about congestion- or accident-risky areas. Hybrid vehicles can also utilize the route prediction for optimizing their charging and discharging phases. In this paper, a new lightweight route recognition approach using instance-based learning is introduced. In this approach, the current route is compared in real-time against the route instances observed in past, and the most similar route is selected. In order to assess the similarity between the routes, a similarity measure based on the longest common subsequence (LCSS) is employed, and an algorithm for incrementally evaluating the LCSS is introduced. The feasibility of the proposed approach is empirically evaluated using real-world data; the obtained results indicate that the routes can be accurately recognized with a delay of 11 turn-points.
{"title":"Real-time recognition of personal routes using instance-based learning","authors":"O. Mazhelis","doi":"10.1109/IVS.2011.5940441","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940441","url":null,"abstract":"Predicting routes is a critical enabler for many new location-based applications and services, such as warning drivers about congestion- or accident-risky areas. Hybrid vehicles can also utilize the route prediction for optimizing their charging and discharging phases. In this paper, a new lightweight route recognition approach using instance-based learning is introduced. In this approach, the current route is compared in real-time against the route instances observed in past, and the most similar route is selected. In order to assess the similarity between the routes, a similarity measure based on the longest common subsequence (LCSS) is employed, and an algorithm for incrementally evaluating the LCSS is introduced. The feasibility of the proposed approach is empirically evaluated using real-world data; the obtained results indicate that the routes can be accurately recognized with a delay of 11 turn-points.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594381","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}
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