Pub Date : 2007-12-01DOI: 10.1109/ICVES.2007.4456385
L. Zhifeng, Wang Jianqiang, L. Keqiang
In order to solve the problem of the instability in the selection of relevant target, caused by the complexity of the measurement environment of vehicular radar, a robust method to determine the relevant target using vehicular radar is proposed. Based on analyzing the measurement environment, the method uses the principle of the nearest object in the same lane for target pre-selection. The Kalman filter is applied to predict the target information and the relevance verification of the pre-selected target is done by the consistence checking. The target decisions are made through a "relevant target life cycle" method. The verification tests show that by efficiently eliminating the effects of ghost objects, other disturbances and the bumping and swinging of vehicle, the proposed method can accomplish the determination of relevant target under different conditions.
{"title":"A robust method to determine relevant target vehicle using vehicular radar","authors":"L. Zhifeng, Wang Jianqiang, L. Keqiang","doi":"10.1109/ICVES.2007.4456385","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456385","url":null,"abstract":"In order to solve the problem of the instability in the selection of relevant target, caused by the complexity of the measurement environment of vehicular radar, a robust method to determine the relevant target using vehicular radar is proposed. Based on analyzing the measurement environment, the method uses the principle of the nearest object in the same lane for target pre-selection. The Kalman filter is applied to predict the target information and the relevance verification of the pre-selected target is done by the consistence checking. The target decisions are made through a \"relevant target life cycle\" method. The verification tests show that by efficiently eliminating the effects of ghost objects, other disturbances and the bumping and swinging of vehicle, the proposed method can accomplish the determination of relevant target under different conditions.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127104619","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456369
Yunfeng Ai, Yuan Sun, Wuling Huang, X. Qiao
With the enormous market potentials of telematics industry and for further gains in safety and convenience, automotive telematics has become a hot R&D area in mobile computing and ITS. Quite a number of telematics services have been proposed by automakers and third-party service providers. This paper describes an OSGi based in-vehicle telematics service platform, by which telematics services can be accessed by end users. Both the hardware and software platforms are analyzed from the application and implementation points of view. Finally, application examples are introduced and some concluding remarks are drawn for this paper.
{"title":"OSGi based integrated service platform for automotive telematics","authors":"Yunfeng Ai, Yuan Sun, Wuling Huang, X. Qiao","doi":"10.1109/ICVES.2007.4456369","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456369","url":null,"abstract":"With the enormous market potentials of telematics industry and for further gains in safety and convenience, automotive telematics has become a hot R&D area in mobile computing and ITS. Quite a number of telematics services have been proposed by automakers and third-party service providers. This paper describes an OSGi based in-vehicle telematics service platform, by which telematics services can be accessed by end users. Both the hardware and software platforms are analyzed from the application and implementation points of view. Finally, application examples are introduced and some concluding remarks are drawn for this paper.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128090868","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456405
J. Suwatthikul, R. McMurran, R.P. Jones
Rapid growth in the deployment of networked electronic control units (ECUs) and enhanced software features within automotive vehicles has occurred over the past two decades. This inevitably results in difficulties and complexity in in-vehicle network fault diagnostics. To overcome these problems, a framework for on-board in-vehicle network diagnostics has been proposed and its concept has previously been demonstrated through experiments. This paper presents a further implementation of network fault detection within the framework. Adaptive OSEK Network Management, a new technique for detecting network level faults, is presented. It is demonstrated in this paper that this technique provides more accurate fault detection and the capability to cover more fault scenarios.
{"title":"Adaptive OSEK Network Management for in-vehicle network fault detection","authors":"J. Suwatthikul, R. McMurran, R.P. Jones","doi":"10.1109/ICVES.2007.4456405","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456405","url":null,"abstract":"Rapid growth in the deployment of networked electronic control units (ECUs) and enhanced software features within automotive vehicles has occurred over the past two decades. This inevitably results in difficulties and complexity in in-vehicle network fault diagnostics. To overcome these problems, a framework for on-board in-vehicle network diagnostics has been proposed and its concept has previously been demonstrated through experiments. This paper presents a further implementation of network fault detection within the framework. Adaptive OSEK Network Management, a new technique for detecting network level faults, is presented. It is demonstrated in this paper that this technique provides more accurate fault detection and the capability to cover more fault scenarios.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132819347","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456361
Kunfeng Wang, Hua Huang, Yuantao Li, Fei-Yue Wang
In this paper, we present a novel camera calibration method which requires only a few easy attainable lane markings in traffic scenes. All we need to know beforehand are a pair of parallel lane markings with known lane width and either the camera height or the length of a land marking parallel to the road. If the camera height is known a-prior, a set of camera parameters such as the focal length, the tilt angle, and the pan angle can be recovered; if the length of a land marking parallel to the road is known a-prior, not only the above camera parameters, but the camera height can also be recovered. We show experimentally that the proposed method is capable of achieving accurate results in most traffic monitoring applications, including inverse perspective transformation and even 3-D estimation of vehicle dimensions.
{"title":"Research on lane-marking line based camera calibration","authors":"Kunfeng Wang, Hua Huang, Yuantao Li, Fei-Yue Wang","doi":"10.1109/ICVES.2007.4456361","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456361","url":null,"abstract":"In this paper, we present a novel camera calibration method which requires only a few easy attainable lane markings in traffic scenes. All we need to know beforehand are a pair of parallel lane markings with known lane width and either the camera height or the length of a land marking parallel to the road. If the camera height is known a-prior, a set of camera parameters such as the focal length, the tilt angle, and the pan angle can be recovered; if the length of a land marking parallel to the road is known a-prior, not only the above camera parameters, but the camera height can also be recovered. We show experimentally that the proposed method is capable of achieving accurate results in most traffic monitoring applications, including inverse perspective transformation and even 3-D estimation of vehicle dimensions.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130599091","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456377
C. Blanc, P. Checchin, S. Gidel, L. Trassoudaine
This paper deals with the assessment of centralized fusion for two dissimilar sensors for the purpose of tracking road obstacles. The aim of sensor fusion is to produce an improved estimated state of a system from a set of independent data sources. Indeed, for a robust perception of the environment, seen here as obstacles, several sensors should be installed in the equipped vehicle: camera, lidar, radar, etc. In our case, the motivation for this work comes from the need to track road targets with lidar measurements combined with radar ones. Thus, the aim is to combine effectively radar range measurements (i.e. range and range rate) with lidar Cartesian measurements for a "turn" scenario. Centralized fusion, i.e. measurement fusion, for two dissimilar sensors is considered here for assessment which is based on Cramer- Rao Lower Bound (CRLB), the basic tool for investigating estimation performance as it represents a limit of cognizability of the state. In the target tracking area, a recursive formulation of the Posterior Cramer-Rao Lower Bound (PCRLB) is used to analyze performance. Many bound comparisons are made according to the scenarios used and various sensor configurations. Moreover, two algorithms for target motion analysis are developed and compared to the theoretical bounds of performance: the extended Kalman filter and the particle filter.
{"title":"Data fusion performance evaluation for range measurements combined with cartesian ones for road obstacle tracking","authors":"C. Blanc, P. Checchin, S. Gidel, L. Trassoudaine","doi":"10.1109/ICVES.2007.4456377","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456377","url":null,"abstract":"This paper deals with the assessment of centralized fusion for two dissimilar sensors for the purpose of tracking road obstacles. The aim of sensor fusion is to produce an improved estimated state of a system from a set of independent data sources. Indeed, for a robust perception of the environment, seen here as obstacles, several sensors should be installed in the equipped vehicle: camera, lidar, radar, etc. In our case, the motivation for this work comes from the need to track road targets with lidar measurements combined with radar ones. Thus, the aim is to combine effectively radar range measurements (i.e. range and range rate) with lidar Cartesian measurements for a \"turn\" scenario. Centralized fusion, i.e. measurement fusion, for two dissimilar sensors is considered here for assessment which is based on Cramer- Rao Lower Bound (CRLB), the basic tool for investigating estimation performance as it represents a limit of cognizability of the state. In the target tracking area, a recursive formulation of the Posterior Cramer-Rao Lower Bound (PCRLB) is used to analyze performance. Many bound comparisons are made according to the scenarios used and various sensor configurations. Moreover, two algorithms for target motion analysis are developed and compared to the theoretical bounds of performance: the extended Kalman filter and the particle filter.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130729912","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456388
Liang Zhao, Fei-Yue Wang
In this paper, an self-organizing TSK-type fuzzy neural network is proposed for predicting the short-term traffic flow. The proposed fuzzy neural network is adaptively organized from the collected short-term traffic flow data. The whole process is divided into two stage, i.e., structure identification and parameter learning. In structure identification, the mean shift clustering algorithm performs the whole traffic flow data set in order to generate the initial structure and mean firing strength method is used to prune the redundant fuzzy neurons. After the structure identification is finished, the chaotic parameter PSO is adopted to perform the parameter learning. Then the trained fuzzy neural network is employed the collected short- term traffic flow test set and the prediction result verifies that the self-organizing TSK-type fuzzy neural network has higher prediction accuracy than some traditional methods.
{"title":"Short-term fuzzy traffic flow prediction using self-organizing TSK-type fuzzy neural network","authors":"Liang Zhao, Fei-Yue Wang","doi":"10.1109/ICVES.2007.4456388","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456388","url":null,"abstract":"In this paper, an self-organizing TSK-type fuzzy neural network is proposed for predicting the short-term traffic flow. The proposed fuzzy neural network is adaptively organized from the collected short-term traffic flow data. The whole process is divided into two stage, i.e., structure identification and parameter learning. In structure identification, the mean shift clustering algorithm performs the whole traffic flow data set in order to generate the initial structure and mean firing strength method is used to prune the redundant fuzzy neurons. After the structure identification is finished, the chaotic parameter PSO is adopted to perform the parameter learning. Then the trained fuzzy neural network is employed the collected short- term traffic flow test set and the prediction result verifies that the self-organizing TSK-type fuzzy neural network has higher prediction accuracy than some traditional methods.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127160145","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456390
R. Niu, Yuan Cao, T. Tang
TTP/C is a member of the time-triggered protocol (TTP) family that satisfies Society of Automotive Engineers Class C requirements for hard real-time fault-tolerant communication. As a communication network designed for safety-critical system, it is essential to verify its safety depending on formal methods. We investigate the fault-tolerant and fault-avoidance strategies of TTP/C network used in Drive-by-wire system, with Markov modeling techniques, and evaluate the failure rate subject to different failure modes, taking into account both transit and permanent physical failures. Generalized Stochastic Petri Net (GSPN) is selected to model concurrency, non-determinism properties and calculate Markov model automatically. A model with 157 states and 78 transitions is built. The result of experiments shows that failure probability of TTP/C network in 7-nodes DBW system varies from 10-6 to 10-10 with different configuration. And diagnose mistakes are proved to be a critical factor for the success of membership service.
{"title":"Formal safety verification for TTP/C network in Drive-by-wire system","authors":"R. Niu, Yuan Cao, T. Tang","doi":"10.1109/ICVES.2007.4456390","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456390","url":null,"abstract":"TTP/C is a member of the time-triggered protocol (TTP) family that satisfies Society of Automotive Engineers Class C requirements for hard real-time fault-tolerant communication. As a communication network designed for safety-critical system, it is essential to verify its safety depending on formal methods. We investigate the fault-tolerant and fault-avoidance strategies of TTP/C network used in Drive-by-wire system, with Markov modeling techniques, and evaluate the failure rate subject to different failure modes, taking into account both transit and permanent physical failures. Generalized Stochastic Petri Net (GSPN) is selected to model concurrency, non-determinism properties and calculate Markov model automatically. A model with 157 states and 78 transitions is built. The result of experiments shows that failure probability of TTP/C network in 7-nodes DBW system varies from 10-6 to 10-10 with different configuration. And diagnose mistakes are proved to be a critical factor for the success of membership service.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131969594","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456397
R. Sosa, G. Velazquez
Insurance companies have notice that since 1984, a couple of years after antilock braking systems (ABS) were introduced in market, traffic accidents and its injuries have been decreased. Developments in electronics and mechanics have improved the vehicle performance in collision. Lately several developments on preventing or avoiding collision have raised as active safety systems or so called in Europe, Advanced Driver Assistance Systems (ADAS). Systems as adaptive cruise control, lane change assist and blind spot detection have been developed facing the challenge of avoiding collision. In present paper is shown a model of collision avoidance for automotive applications. The system includes a model for vehicle dynamics: it was developed with the causal software AMESIM. Decision functions were developed to determine when an object is a dangerous obstacle, those functions depends on relative speed, and distance between host vehicle and obstacle. Vehicle model and decision functions are integrated to become a system for collision avoidance. The system warns the driver in a distance safe enough to avoid the collision in case the driver neglects warnings, the system begins braking in order to decrease damage severity if the collision happens or even avoid it. The simulation results of selected collision scenarios are presented. Also a brief description of available sensors for this application is shown.
{"title":"Obstacles detection and collision avoidance system developed with virtual models","authors":"R. Sosa, G. Velazquez","doi":"10.1109/ICVES.2007.4456397","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456397","url":null,"abstract":"Insurance companies have notice that since 1984, a couple of years after antilock braking systems (ABS) were introduced in market, traffic accidents and its injuries have been decreased. Developments in electronics and mechanics have improved the vehicle performance in collision. Lately several developments on preventing or avoiding collision have raised as active safety systems or so called in Europe, Advanced Driver Assistance Systems (ADAS). Systems as adaptive cruise control, lane change assist and blind spot detection have been developed facing the challenge of avoiding collision. In present paper is shown a model of collision avoidance for automotive applications. The system includes a model for vehicle dynamics: it was developed with the causal software AMESIM. Decision functions were developed to determine when an object is a dangerous obstacle, those functions depends on relative speed, and distance between host vehicle and obstacle. Vehicle model and decision functions are integrated to become a system for collision avoidance. The system warns the driver in a distance safe enough to avoid the collision in case the driver neglects warnings, the system begins braking in order to decrease damage severity if the collision happens or even avoid it. The simulation results of selected collision scenarios are presented. Also a brief description of available sensors for this application is shown.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131787673","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}
Location-aware computing becomes an exciting research as recent advancements in RF circuits and wireless communication stacks. In this paper, we present a fingerprinting based location estimation technology in ZigBee network. The system uses the signal strength from several base stations rather than time or angle for determining the location of mobile station. Instead of modeling the complex attenuation of signal strength, the system models the probabilistic distribution in different geographical areas which we called fingerprinting. It combines the measured data and fingerprinting to determine the mobile station's location. The experiment results demonstrate the validity of location estimation in ZigBee network based on fingerprinting.
{"title":"Location estimation in ZigBee Network based on fingerprinting","authors":"Qingming Yao, Fei-Yue Wang, Hui Gao, Kunfeng Wang, Hongxia Zhao","doi":"10.1109/ICVES.2007.4456358","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456358","url":null,"abstract":"Location-aware computing becomes an exciting research as recent advancements in RF circuits and wireless communication stacks. In this paper, we present a fingerprinting based location estimation technology in ZigBee network. The system uses the signal strength from several base stations rather than time or angle for determining the location of mobile station. Instead of modeling the complex attenuation of signal strength, the system models the probabilistic distribution in different geographical areas which we called fingerprinting. It combines the measured data and fingerprinting to determine the mobile station's location. The experiment results demonstrate the validity of location estimation in ZigBee network based on fingerprinting.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129099543","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 : 2007-12-01DOI: 10.1109/ICVES.2007.4456365
L. de Oliveira Neris, V.O. Roda, O. Trindade
Automatic guidance systems have became important in agriculture to reduce the operator's work and fatigue, resulting in higher efficiency and safety in field operations. Most crops are cultivated in rows, hence enabling the development of a row-recognition system to accurately follow a row of plants without overrunning the crop. This paper presents a novel approach for extracting agricultural machine position from field images based on the look-ahead method and vanishing points. The proposed method facilitates the development of the guidance controller, simplifying the tuning and increasing the guidance system response and stability. The goal was to develop a quick position extraction method, based on the fact that by decreasing the image processing time increases the position data rate, providing fast response feedback control. Results demonstrated that the use of look- ahead method and vanishing point simplifies the guidance controller development and satisfactorily works with shadows, gaps, weeds and different light conditions.
{"title":"A method for agricultural machine guidance on row crops based on the vanishing point","authors":"L. de Oliveira Neris, V.O. Roda, O. Trindade","doi":"10.1109/ICVES.2007.4456365","DOIUrl":"https://doi.org/10.1109/ICVES.2007.4456365","url":null,"abstract":"Automatic guidance systems have became important in agriculture to reduce the operator's work and fatigue, resulting in higher efficiency and safety in field operations. Most crops are cultivated in rows, hence enabling the development of a row-recognition system to accurately follow a row of plants without overrunning the crop. This paper presents a novel approach for extracting agricultural machine position from field images based on the look-ahead method and vanishing points. The proposed method facilitates the development of the guidance controller, simplifying the tuning and increasing the guidance system response and stability. The goal was to develop a quick position extraction method, based on the fact that by decreasing the image processing time increases the position data rate, providing fast response feedback control. Results demonstrated that the use of look- ahead method and vanishing point simplifies the guidance controller development and satisfactorily works with shadows, gaps, weeds and different light conditions.","PeriodicalId":202772,"journal":{"name":"2007 IEEE International Conference on Vehicular Electronics and Safety","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177986","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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