Pub Date : 2011-06-05DOI: 10.1109/IVS.2011.5940453
D. V. Hamme, P. Veelaert, W. Philips
GPS by itself is not dependable in urban environments, due to signal reception issues such as multi-path effects or occlusion. Other sensor data is required to keep track of the vehicle in absence of a reliable GPS signal. We propose a new method to use a single on-board consumer-grade camera for vehicle motion estimation. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Experimental results show good accuracy and high reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance.
{"title":"Robust monocular visual odometry by uncertainty voting","authors":"D. V. Hamme, P. Veelaert, W. Philips","doi":"10.1109/IVS.2011.5940453","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940453","url":null,"abstract":"GPS by itself is not dependable in urban environments, due to signal reception issues such as multi-path effects or occlusion. Other sensor data is required to keep track of the vehicle in absence of a reliable GPS signal. We propose a new method to use a single on-board consumer-grade camera for vehicle motion estimation. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Experimental results show good accuracy and high reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"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":"129948190","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.2011.5940517
M. Dawood, C. Cappelle, Maan El Badaoui El Najjar, Mohamad Khalil, D. Pomorski
Vehicle geo-localization in urban areas remains to be challenging problems. For this purpose, Global Positioning System (GPS) receiver is usually the main sensor. But, the use of GPS alone is not sufficient in many urban environments due to wave multi-path. In order to provide accurate and robust localization, GPS has so to be helped with other sensors like dead-reckoned sensors, map data, cameras or LIDAR. In this paper, a new observation of the absolute pose of the vehicle is proposed to back up GPS measurements. The proposed approach exploits a virtual 3D model managed by a 3D geographical information system (3D GIS) and a video camera. The concept is to register the acquired image to the 3D model that is geo-localized. For that, two images have to be matched: the real image and the virtual image. The real image is acquired by the on board camera and provides the real view of the scene viewed by the vehicle. The virtual image is provided by the 3D GIS. The developed method is composed of three parts. The first part consists in detecting and matching the feature points of the real image and of the virtual image. Two methods: SIFT (Scale Invariant Feature Transform) and Harris corner detector are compared. The second part concerns the position computation using POSIT algorithm and the previously matched features set. The third part concerns the data fusion using IMM-UKF (Interacting Multiple Model-Unscented Kalman Filter). The proposed approach has been tested on a real sequence and the obtained results proved the feasibility and robustness of the approach.
{"title":"Vehicle geo-localization based on IMM-UKF data fusion using a GPS receiver, a video camera and a 3D city model","authors":"M. Dawood, C. Cappelle, Maan El Badaoui El Najjar, Mohamad Khalil, D. Pomorski","doi":"10.1109/IVS.2011.5940517","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940517","url":null,"abstract":"Vehicle geo-localization in urban areas remains to be challenging problems. For this purpose, Global Positioning System (GPS) receiver is usually the main sensor. But, the use of GPS alone is not sufficient in many urban environments due to wave multi-path. In order to provide accurate and robust localization, GPS has so to be helped with other sensors like dead-reckoned sensors, map data, cameras or LIDAR. In this paper, a new observation of the absolute pose of the vehicle is proposed to back up GPS measurements. The proposed approach exploits a virtual 3D model managed by a 3D geographical information system (3D GIS) and a video camera. The concept is to register the acquired image to the 3D model that is geo-localized. For that, two images have to be matched: the real image and the virtual image. The real image is acquired by the on board camera and provides the real view of the scene viewed by the vehicle. The virtual image is provided by the 3D GIS. The developed method is composed of three parts. The first part consists in detecting and matching the feature points of the real image and of the virtual image. Two methods: SIFT (Scale Invariant Feature Transform) and Harris corner detector are compared. The second part concerns the position computation using POSIT algorithm and the previously matched features set. The third part concerns the data fusion using IMM-UKF (Interacting Multiple Model-Unscented Kalman Filter). The proposed approach has been tested on a real sequence and the obtained results proved the feasibility and robustness of the approach.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"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":"128556246","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.2011.5940476
M. Schmid, S. Ateş, J. Dickmann, F. V. Hundelshausen, Hans-Joachim Wünsche
An automatic parking system relies on precise estimation of parking space geometry.
自动停车系统依赖于车位几何形状的精确估计。
{"title":"Parking space detection with hierarchical dynamic occupancy grids","authors":"M. Schmid, S. Ateş, J. Dickmann, F. V. Hundelshausen, Hans-Joachim Wünsche","doi":"10.1109/IVS.2011.5940476","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940476","url":null,"abstract":"An automatic parking system relies on precise estimation of parking space geometry.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"6 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":"128639214","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.2011.5940580
F. Oniga, S. Nedevschi
In this paper we present a real-time algorithm that detects curbs using a cubic spline model. A Digital Elevation Map (DEM) is used to represent the dense stereovision data. Curb measurements (cells) are detected on the current frame DEM. In order to compensate the small number of curb measurements for each frame we perform temporal integration. The result is a rich set of curb measurements that provides a good support for the least square cubic spline fitting. Thus, the curb cubic spline approximation is more stable and available on a much larger area, around the ego car. This compensates the limited field of view of typical stereo sensors. The detected curbs enrich the description of the ego car's surrounding 3D environment and can be used for driving assistance applications.
{"title":"Curb detection for driving assistance systems: A cubic spline-based approach","authors":"F. Oniga, S. Nedevschi","doi":"10.1109/IVS.2011.5940580","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940580","url":null,"abstract":"In this paper we present a real-time algorithm that detects curbs using a cubic spline model. A Digital Elevation Map (DEM) is used to represent the dense stereovision data. Curb measurements (cells) are detected on the current frame DEM. In order to compensate the small number of curb measurements for each frame we perform temporal integration. The result is a rich set of curb measurements that provides a good support for the least square cubic spline fitting. Thus, the curb cubic spline approximation is more stable and available on a much larger area, around the ego car. This compensates the limited field of view of typical stereo sensors. The detected curbs enrich the description of the ego car's surrounding 3D environment and can be used for driving assistance applications.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"21 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":"129336013","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.2011.5940552
Michael Müter, Naim Asaj
Due to an increased connectivity and seamless integration of information technology into modern vehicles, a trend of research in the automotive domain is the development of holistic IT security concepts. Within the scope of this development, vehicular attack detection is one concept which gains an increased attention, because of its reactive nature that allows to respond to threats during runtime. In this paper we explore the applicability of entropy-based attack detection for in-vehicle networks. We illustrate the crucial aspects for an adaptation of such an approach to the automotive domain. Moreover, we show first exemplary results by applying the approach to measurements derived from a standard vehicle's CAN-Body network.
{"title":"Entropy-based anomaly detection for in-vehicle networks","authors":"Michael Müter, Naim Asaj","doi":"10.1109/IVS.2011.5940552","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940552","url":null,"abstract":"Due to an increased connectivity and seamless integration of information technology into modern vehicles, a trend of research in the automotive domain is the development of holistic IT security concepts. Within the scope of this development, vehicular attack detection is one concept which gains an increased attention, because of its reactive nature that allows to respond to threats during runtime. In this paper we explore the applicability of entropy-based attack detection for in-vehicle networks. We illustrate the crucial aspects for an adaptation of such an approach to the automotive domain. Moreover, we show first exemplary results by applying the approach to measurements derived from a standard vehicle's CAN-Body network.","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":"130528737","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.2011.5940477
L. Marcaccioli, E. Sbarra, L. Urbani, R. V. Gatti, R. Sorrentino
This paper presents an innovative accurate ranging system based on FMCW (Frequency Modulated Continuous Wave) Radar. Thanks to a combination of nontrivial solutions a centimetre precision is guaranteed even in tough environments, such as warehouses packed with metallic objects or extremely dusty sites, where conventional RF, laser, ultrasonic or video technology may present severe limitations. The proposed solution is currently being applied in Automatic Guided Vehicle for warehouses. The architecture of the system and the obtained results are presented in details.
{"title":"An accurate indoor ranging system based on FMCW radar","authors":"L. Marcaccioli, E. Sbarra, L. Urbani, R. V. Gatti, R. Sorrentino","doi":"10.1109/IVS.2011.5940477","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940477","url":null,"abstract":"This paper presents an innovative accurate ranging system based on FMCW (Frequency Modulated Continuous Wave) Radar. Thanks to a combination of nontrivial solutions a centimetre precision is guaranteed even in tough environments, such as warehouses packed with metallic objects or extremely dusty sites, where conventional RF, laser, ultrasonic or video technology may present severe limitations. The proposed solution is currently being applied in Automatic Guided Vehicle for warehouses. The architecture of the system and the obtained results are presented in details.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"51 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":"130665372","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.2011.5940519
A. Wimmer, R. Graf, K. Dietmayer
Detailed and highly accurate digital maps provide useful information for future driver assistance systems. The information of the positions of infrastructure objects and lane markings can be used to extend the knowledge of the environment obtained by local sensors. To exploit highly accurate maps, the exact position of the vehicle within the map must be known. For that, a rough localization with standard GPS is extended by matching objects detected with a laser scanner and data from the map. The paper focuses on road construction sites, which are a demanding environment for sensorial perception and interpretation. The matching algorithms are based on beacons, which are commonly used infrastructure elements at road works.
{"title":"Matching highly accurate maps to local environmental perception at road construction sites","authors":"A. Wimmer, R. Graf, K. Dietmayer","doi":"10.1109/IVS.2011.5940519","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940519","url":null,"abstract":"Detailed and highly accurate digital maps provide useful information for future driver assistance systems. The information of the positions of infrastructure objects and lane markings can be used to extend the knowledge of the environment obtained by local sensors. To exploit highly accurate maps, the exact position of the vehicle within the map must be known. For that, a rough localization with standard GPS is extended by matching objects detected with a laser scanner and data from the map. The paper focuses on road construction sites, which are a demanding environment for sensorial perception and interpretation. The matching algorithms are based on beacons, which are commonly used infrastructure elements at road works.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"79 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120897307","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.2011.5940502
Chunzhao Guo, Wataru Sato, Long Han, S. Mita, David A. McAllester
Comprehensive situational awareness is paramount to the effectiveness of proprietary navigational and higher-level functions of intelligent vehicles. In this paper, we address a graph-based approach for 2D road representation of 3D point clouds with respect to the road topography. We employ the gradient cues of the road geometry to construct a Markov Random Filed (MRF) and implement an efficient belief propagation (BP) algorithm to classify the road environment into four categories, i.e. the reachable region, the drivable region, the obstacle region and the unknown region. The proposed approach can overcome a wide variety of practical challenges, such as sloped terrains, rough road surfaces, rolling/pitching of the host vehicle, etc., and represent the road environment accurately as well as robustly. Experimental results in typical but challenging environments have substantiated that the proposed approach is more sensitive and reliable than the conventional vertical displacements analysis and show superior performance against other local classifiers.
{"title":"Graph-based 2D road representation of 3D point clouds for intelligent vehicles","authors":"Chunzhao Guo, Wataru Sato, Long Han, S. Mita, David A. McAllester","doi":"10.1109/IVS.2011.5940502","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940502","url":null,"abstract":"Comprehensive situational awareness is paramount to the effectiveness of proprietary navigational and higher-level functions of intelligent vehicles. In this paper, we address a graph-based approach for 2D road representation of 3D point clouds with respect to the road topography. We employ the gradient cues of the road geometry to construct a Markov Random Filed (MRF) and implement an efficient belief propagation (BP) algorithm to classify the road environment into four categories, i.e. the reachable region, the drivable region, the obstacle region and the unknown region. The proposed approach can overcome a wide variety of practical challenges, such as sloped terrains, rough road surfaces, rolling/pitching of the host vehicle, etc., and represent the road environment accurately as well as robustly. Experimental results in typical but challenging environments have substantiated that the proposed approach is more sensitive and reliable than the conventional vertical displacements analysis and show superior performance against other local classifiers.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"39 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":"116282047","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.2011.5940492
J. Connell, Benjamin Herta, Sharath Pankanti, H. Hess, Sebastian Pliefke
We describe a system that controls whether the headlights of a vehicle are in the highbeam or lowbeam state based on input from a forward looking video camera. The core of the system relies on conventional computer vision techniques, albeit with a sophisticated spot finder front-end. Despite this architecture we are able to use an automated supervised learning technique to tune the system to yield high performance. Using a customer-imposed metric we present both in-car and off-line results from our system along with several competitors, and investigate the system's performance under different weather conditions.
{"title":"A fast and robust intelligent headlight controller for vehicles","authors":"J. Connell, Benjamin Herta, Sharath Pankanti, H. Hess, Sebastian Pliefke","doi":"10.1109/IVS.2011.5940492","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940492","url":null,"abstract":"We describe a system that controls whether the headlights of a vehicle are in the highbeam or lowbeam state based on input from a forward looking video camera. The core of the system relies on conventional computer vision techniques, albeit with a sophisticated spot finder front-end. Despite this architecture we are able to use an automated supervised learning technique to tune the system to yield high performance. Using a customer-imposed metric we present both in-car and off-line results from our system along with several competitors, and investigate the system's performance under different weather conditions.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"236 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":"124058352","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.2011.5940530
M. Eilers, C. Möbus
Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-learning alternative by estimating stochastic driver models from behavior traces. They are more robust than their non-stochastic predecessors. In this paper we present a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) model for the longitudinal control of human drivers in an inner-city traffic scenario. It is learnt on the basis of multivariate time-series obtained in simulator studies. Percepts relevant for longitudinal control were included in the model by a structure-learning method using Bayesian information criteria. Besides mimicking human driver behavior we suggest using the model for prototyping intelligent assistance systems with human-like behavior.
{"title":"Learning the human longitudinal control behavior with a modular hierarchical Bayesian Mixture-of-Behaviors model","authors":"M. Eilers, C. Möbus","doi":"10.1109/IVS.2011.5940530","DOIUrl":"https://doi.org/10.1109/IVS.2011.5940530","url":null,"abstract":"Modeling drivers' behavior is believed to be essential for the rapid prototyping of error-compensating assistance systems. Various authors proposed control-theoretic and production-system models. These models are handcrafted in a top-down software engineering process. Here we propose a machine-learning alternative by estimating stochastic driver models from behavior traces. They are more robust than their non-stochastic predecessors. In this paper we present a Bayesian Autonomous Driver Mixture-of-Behaviors (BAD MoB) model for the longitudinal control of human drivers in an inner-city traffic scenario. It is learnt on the basis of multivariate time-series obtained in simulator studies. Percepts relevant for longitudinal control were included in the model by a structure-learning method using Bayesian information criteria. Besides mimicking human driver behavior we suggest using the model for prototyping intelligent assistance systems with human-like behavior.","PeriodicalId":117811,"journal":{"name":"2011 IEEE Intelligent Vehicles Symposium (IV)","volume":"103 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":"128014991","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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