Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856481
Sebastian Schneider, G. Mueller, Jan Kallwies, Hans-Joachim Wünsche
Obstacle avoidance is a key feature for automotive navigation that requires an accurate representation of the environment. In the field of visual perception this task has often been addressed with stereo algorithms that try to obtain a depth map of the environment via disparity calculations on a single pair of images. These algorithms do not exploit that especially in automotive scenarios the fields of view between two consecutive frames have large overlapping regions. Instead, the disparity map is computed from scratch for each stereo frame and no information is propagated from one frame to the next. Since monocular image processing has long benefited from recursive estimation techniques, such as the 4D Approach, this paper presents a novel recursive automotive stereo algorithm, called RAS. RAS internally maintains a list of recursively estimated 3D points that are continuously updated based on the vehicle's movement and measurements in the current stereo frame. We show that RAS not only preserves the knowledge of the environment across frames, but also accounts for measurement modalities and is robust against faulty or even missing measurements.
{"title":"RAS: Recursive automotive stereo","authors":"Sebastian Schneider, G. Mueller, Jan Kallwies, Hans-Joachim Wünsche","doi":"10.1109/IVS.2014.6856481","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856481","url":null,"abstract":"Obstacle avoidance is a key feature for automotive navigation that requires an accurate representation of the environment. In the field of visual perception this task has often been addressed with stereo algorithms that try to obtain a depth map of the environment via disparity calculations on a single pair of images. These algorithms do not exploit that especially in automotive scenarios the fields of view between two consecutive frames have large overlapping regions. Instead, the disparity map is computed from scratch for each stereo frame and no information is propagated from one frame to the next. Since monocular image processing has long benefited from recursive estimation techniques, such as the 4D Approach, this paper presents a novel recursive automotive stereo algorithm, called RAS. RAS internally maintains a list of recursively estimated 3D points that are continuously updated based on the vehicle's movement and measurements in the current stereo frame. We show that RAS not only preserves the knowledge of the environment across frames, but also accounts for measurement modalities and is robust against faulty or even missing measurements.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131919131","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856602
A. Albousefi, H. Ying, Dimitar Filev, F. Syed, K. Prakah-Asante, F. Tseng, Hsin-Hsiang Yang
Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system may assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g, lane departure) and alert the driver to take corrective action. In this paper, we show how the support vector machine (SVM) methodology can potentially provide enhanced unintentional lane departure prediction, which is a new method relative to literature. Our binary SVM employed the Radial Basis Function kernel to classify time series of select vehicle variables. The SVM was trained and tested using the driver experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company. The data that we used represented 16 drowsy subjects (three-hour driving time per subject) and six control subjects (20 minutes driving per subject), all of which drove a simulated 2000 Volvo S80. The vehicle variables were all sampled at 50 Hz. There were a total of 3,508 unintentional lane departure occurrences for the drowsy drivers and only 23 for four of the six control drivers (two had none). The SVM was trained by over 60,000 time series examples (the actual number depended on the prediction horizon) created from 50% of the lane departures. The training data were removed from the testing data. During the testing, the SVM made a lane departure prediction at every sampling time for every one of the 22 drivers (over 6.8 million predictions in total). The overall sensitivity and specificity of the SVM with a 0.2-second prediction horizon for the 22 drivers were 99.77465% and 99.99997%, respectively. The SVM predicted, on average 0.200181 seconds in advance, lane departure correctly for all the control drivers, but missed 4 of the 1,758 and gave false positives for another 2 for the drowsy drivers. For the prediction horizon of 0.4s, there was 1 false positive case for the control subjects, and the false negative and false positive cases rose substantially to 10 and 137 for the drowsy drivers, respectively.
{"title":"A support vector machine approach to unintentional vehicle lane departure prediction","authors":"A. Albousefi, H. Ying, Dimitar Filev, F. Syed, K. Prakah-Asante, F. Tseng, Hsin-Hsiang Yang","doi":"10.1109/IVS.2014.6856602","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856602","url":null,"abstract":"Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and R & D efforts. Such a system may assist the driver by monitoring the driver or vehicle behaviors to predict/detect driving situations (e.g, lane departure) and alert the driver to take corrective action. In this paper, we show how the support vector machine (SVM) methodology can potentially provide enhanced unintentional lane departure prediction, which is a new method relative to literature. Our binary SVM employed the Radial Basis Function kernel to classify time series of select vehicle variables. The SVM was trained and tested using the driver experiment data generated by VIRTTEX, a hydraulically powered 6-degrees-of-freedom moving base driving simulator at Ford Motor Company. The data that we used represented 16 drowsy subjects (three-hour driving time per subject) and six control subjects (20 minutes driving per subject), all of which drove a simulated 2000 Volvo S80. The vehicle variables were all sampled at 50 Hz. There were a total of 3,508 unintentional lane departure occurrences for the drowsy drivers and only 23 for four of the six control drivers (two had none). The SVM was trained by over 60,000 time series examples (the actual number depended on the prediction horizon) created from 50% of the lane departures. The training data were removed from the testing data. During the testing, the SVM made a lane departure prediction at every sampling time for every one of the 22 drivers (over 6.8 million predictions in total). The overall sensitivity and specificity of the SVM with a 0.2-second prediction horizon for the 22 drivers were 99.77465% and 99.99997%, respectively. The SVM predicted, on average 0.200181 seconds in advance, lane departure correctly for all the control drivers, but missed 4 of the 1,758 and gave false positives for another 2 for the drowsy drivers. For the prediction horizon of 0.4s, there was 1 false positive case for the control subjects, and the false negative and false positive cases rose substantially to 10 and 137 for the drowsy drivers, respectively.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134446619","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856587
Nasser Nowdehi, T. Olovsson
Cooperative intelligent transport systems supporting secure vehicle to vehicle and vehicle to infrastructure communications, is becoming a very important topic. The aim of this paper is to share our experiences from implementing the ETSI Intelligent Transport System (ITS) SecuredMessage and sign/verify services on an existing ETSI ITS communication stack (ITSC). We have followed the new ETSI TS 103 097 v1.1.1 standard when implementing the security services, and have made our best to create a robust and secure implementation. Our goal has been to identify flaws and vulnerabilities in our implementation that are caused by weaknesses or deficiencies in the standard and in its description of services. We have then performed an analysis of the protocol, its headers and created test cases used to test our implementation. Several problems were found, and we have also repeated the tests with another, supposedly very stable implementation, provided by Fraunhofer FOKUS. To our surprise, this system also showed unexpected behavior as our system. We show that these problems are the result of weaknesses and complexities in the design of the standard. We present the problems found in our implementation and show what part in the standard was causing the problems. We show that several problems in the standard, mainly due to their complexity, open up for misinterpretation leading to various types of implementation errors. We conclude the paper with proposing changes to the standard to prevent other implementations from repeating the same mistakes.
{"title":"Experiences from implementing the ETSI ITS SecuredMessage service","authors":"Nasser Nowdehi, T. Olovsson","doi":"10.1109/IVS.2014.6856587","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856587","url":null,"abstract":"Cooperative intelligent transport systems supporting secure vehicle to vehicle and vehicle to infrastructure communications, is becoming a very important topic. The aim of this paper is to share our experiences from implementing the ETSI Intelligent Transport System (ITS) SecuredMessage and sign/verify services on an existing ETSI ITS communication stack (ITSC). We have followed the new ETSI TS 103 097 v1.1.1 standard when implementing the security services, and have made our best to create a robust and secure implementation. Our goal has been to identify flaws and vulnerabilities in our implementation that are caused by weaknesses or deficiencies in the standard and in its description of services. We have then performed an analysis of the protocol, its headers and created test cases used to test our implementation. Several problems were found, and we have also repeated the tests with another, supposedly very stable implementation, provided by Fraunhofer FOKUS. To our surprise, this system also showed unexpected behavior as our system. We show that these problems are the result of weaknesses and complexities in the design of the standard. We present the problems found in our implementation and show what part in the standard was causing the problems. We show that several problems in the standard, mainly due to their complexity, open up for misinterpretation leading to various types of implementation errors. We conclude the paper with proposing changes to the standard to prevent other implementations from repeating the same mistakes.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134541956","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856501
S. Grubwinkler, Martin Hirschvogel, M. Lienkamp
This paper presents a system for the prediction of the necessary energy for selected trips of electric vehicles (EVs), which can be used for various EV assistants like range estimation. We use statistical features extracted from crowd-sourced speed profiles for the energy prediction, since they consider the varying impact factors of the individual driving style and the prevailing traffic condition. A statistical prediction model uses these features in order to predict the deviation from the mean energy consumption of the EV. Hence, the model predicts the variance of energy consumption caused for example by individual driving behavior. The results show an improvement of the energy prediction by 5.4 percentage points if the statistical features are considered. The prediction of the propulsion energy for EVs before the start of a given route has a relative mean error of 6.8%.
{"title":"Driver- and situation-specific impact factors for the energy prediction of EVs based on crowd-sourced speed profiles","authors":"S. Grubwinkler, Martin Hirschvogel, M. Lienkamp","doi":"10.1109/IVS.2014.6856501","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856501","url":null,"abstract":"This paper presents a system for the prediction of the necessary energy for selected trips of electric vehicles (EVs), which can be used for various EV assistants like range estimation. We use statistical features extracted from crowd-sourced speed profiles for the energy prediction, since they consider the varying impact factors of the individual driving style and the prevailing traffic condition. A statistical prediction model uses these features in order to predict the deviation from the mean energy consumption of the EV. Hence, the model predicts the variance of energy consumption caused for example by individual driving behavior. The results show an improvement of the energy prediction by 5.4 percentage points if the statistical features are considered. The prediction of the propulsion energy for EVs before the start of a given route has a relative mean error of 6.8%.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131565988","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856393
Peter Nilsson, L. Laine, B. Jacobson
This paper presents a driving simulator experiment in which manual and automated driving of a prospective long vehicle combination has been studied. Based on post analysis of manual and automated driving trajectories, characteristic measures reflecting the manual drivers behavior have been proposed. It was observed that the drivers had a round shape of the utilized accelerations while negotiating the curves. A similar shape was found when using an objective function which included minimizing the resultant jerk vector.
{"title":"Performance characteristics for automated driving of long heavy vehicle combinations evaluated in motion simulator","authors":"Peter Nilsson, L. Laine, B. Jacobson","doi":"10.1109/IVS.2014.6856393","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856393","url":null,"abstract":"This paper presents a driving simulator experiment in which manual and automated driving of a prospective long vehicle combination has been studied. Based on post analysis of manual and automated driving trajectories, characteristic measures reflecting the manual drivers behavior have been proposed. It was observed that the drivers had a round shape of the utilized accelerations while negotiating the curves. A similar shape was found when using an objective function which included minimizing the resultant jerk vector.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127594557","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856428
Richard Matthaei, Gerrit Bagschik, M. Maurer
Future advanced driver assistant systems put high demands on the environmental perception especially in urban environments. Today's on-board sensors and on-board algorithms still do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support the on-board sensor system and algorithms. The usage of map data requires a highly correct pose within the map even in cases of positioning errors by global navigation satellite systems or geometrical errors in the map data. In this paper we propose and compare two approaches for map-relative localization exclusively using a lane-level map. These approaches deliberately avoid the usage of detailed a priori maps containing point-landmarks, grids or road-markings. Additionally, we propose a grid-based on-board fusion of road-marking information and stationary obstacles addressing the problem of missing or incomplete road-markings in urban scenarios.
{"title":"Map-relative localization in lane-level maps for ADAS and autonomous driving","authors":"Richard Matthaei, Gerrit Bagschik, M. Maurer","doi":"10.1109/IVS.2014.6856428","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856428","url":null,"abstract":"Future advanced driver assistant systems put high demands on the environmental perception especially in urban environments. Today's on-board sensors and on-board algorithms still do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support the on-board sensor system and algorithms. The usage of map data requires a highly correct pose within the map even in cases of positioning errors by global navigation satellite systems or geometrical errors in the map data. In this paper we propose and compare two approaches for map-relative localization exclusively using a lane-level map. These approaches deliberately avoid the usage of detailed a priori maps containing point-landmarks, grids or road-markings. Additionally, we propose a grid-based on-board fusion of road-marking information and stationary obstacles addressing the problem of missing or incomplete road-markings in urban scenarios.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133303610","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856523
Hailing Zhou, Hui Kong, J. Álvarez, D. Creighton, S. Nahavandi
We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.
{"title":"Fast road detection and tracking in aerial videos","authors":"Hailing Zhou, Hui Kong, J. Álvarez, D. Creighton, S. Nahavandi","doi":"10.1109/IVS.2014.6856523","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856523","url":null,"abstract":"We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133224556","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856608
Md Tanveer Hayat, Hyungjun Park, Brian L. Smith
The Connected Vehicle enabled Freeway Merge Assistance system is developed by the University of Virginia Center for Transportation Studies, with the aim of reducing conflicts between merging vehicles in freeway ramp area. Initial simulation evaluation results showed that the merge assistance system has significant potential to increase capacity of freeway merge areas and reduce accidents by minimizing the number of conflicts between vehicles. As a next step of evaluation, a field test is conducted at a Connected Vehicle test bed to investigate drivers' response to the personalized advisories relayed by this system. This paper provides an overview of the field test methodology, system architecture, stated preference survey and presents preliminary results for this prototype freeway merge assistance system developed for the Connected Vehicle Environment. The revealed and stated preference data gathered will be used to develop an advisory response model that will incorporate drivers' response variability in the simulation evaluation framework of the freeway merge assistance system.
{"title":"Connected Vehicle Enabled Freeway Merge Assistance system-field test: Preliminary results of driver compliance to advisory","authors":"Md Tanveer Hayat, Hyungjun Park, Brian L. Smith","doi":"10.1109/IVS.2014.6856608","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856608","url":null,"abstract":"The Connected Vehicle enabled Freeway Merge Assistance system is developed by the University of Virginia Center for Transportation Studies, with the aim of reducing conflicts between merging vehicles in freeway ramp area. Initial simulation evaluation results showed that the merge assistance system has significant potential to increase capacity of freeway merge areas and reduce accidents by minimizing the number of conflicts between vehicles. As a next step of evaluation, a field test is conducted at a Connected Vehicle test bed to investigate drivers' response to the personalized advisories relayed by this system. This paper provides an overview of the field test methodology, system architecture, stated preference survey and presents preliminary results for this prototype freeway merge assistance system developed for the Connected Vehicle Environment. The revealed and stated preference data gathered will be used to develop an advisory response model that will incorporate drivers' response variability in the simulation evaluation framework of the freeway merge assistance system.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115552595","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856430
James R. Ward, Gabriel Agamennoni, Stewart Worrall, E. Nebot
Vehicle-to-vehicle (V2V) communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method for collision probability calculation that addresses these problems.
{"title":"Vehicle collision probability calculation for general traffic scenarios under uncertainty","authors":"James R. Ward, Gabriel Agamennoni, Stewart Worrall, E. Nebot","doi":"10.1109/IVS.2014.6856430","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856430","url":null,"abstract":"Vehicle-to-vehicle (V2V) communication systems allow vehicles to share state information with one another to improve safety and efficiency of transportation networks. One of the key applications of such a system is in the prediction and avoidance of collisions between vehicles. If a method to do this is to succeed it must be robust to measurement uncertainty. The method should also be general enough that it does not rely on constraints on vehicle motion for the accuracy of its predictions. It should work for all interactions between vehicles and not just a select subset. This paper presents a method for collision probability calculation that addresses these problems.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115706658","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 : 2014-06-08DOI: 10.1109/IVS.2014.6856547
Christian Fuchs, Simon Eggert, Benjamin Knopp, Dieter Zöbel
The knowledge about relative orientations between truck and trailer is a vital prerequisite for driver assistance systems especially when dealing with safety improvements for this kind of vehicles. Yet, no adequate system solving this problem is available. A sensor system measuring this desired state information by an optical approach is presented in this paper. The sensor system is evaluated using a virtual testbed that has been developed for testing, diagnosis and proper configuration of the sensor.
{"title":"Pose detection in truck and trailer combinations for advanced driver assistance systems","authors":"Christian Fuchs, Simon Eggert, Benjamin Knopp, Dieter Zöbel","doi":"10.1109/IVS.2014.6856547","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856547","url":null,"abstract":"The knowledge about relative orientations between truck and trailer is a vital prerequisite for driver assistance systems especially when dealing with safety improvements for this kind of vehicles. Yet, no adequate system solving this problem is available. A sensor system measuring this desired state information by an optical approach is presented in this paper. The sensor system is evaluated using a virtual testbed that has been developed for testing, diagnosis and proper configuration of the sensor.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114392050","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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