Pub Date : 2014-06-08DOI: 10.1109/IVS.2014.6856548
R. Moalla, B. Lonc, H. Labiod, N. Simoni
Automotive industry is interested to ITS as it provides attractive services for connected vehicles aiming at creating a new and very promising business market. However, the security remains a major challenge for the ITS deployment as several critical threats has been identified. In this paper, we present our Cooperative ITS Vehicle Application Oriented Security Framework CIVAS for vehicle ITS stations. Our focus is given first to ITS vehicle security requirements and constraints. Then, we provide an overview of the “Defense-in-Depth” model that we adapted for securing ITS vehicle. The proposed security framework follows a modular design and is application oriented. To illustrate the interaction between designed CIVAS layers, we consider a vehicle stationary as example of a road safety use case.
{"title":"Towards a Cooperative ITS Vehicle Application Oriented Security Framework","authors":"R. Moalla, B. Lonc, H. Labiod, N. Simoni","doi":"10.1109/IVS.2014.6856548","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856548","url":null,"abstract":"Automotive industry is interested to ITS as it provides attractive services for connected vehicles aiming at creating a new and very promising business market. However, the security remains a major challenge for the ITS deployment as several critical threats has been identified. In this paper, we present our Cooperative ITS Vehicle Application Oriented Security Framework CIVAS for vehicle ITS stations. Our focus is given first to ITS vehicle security requirements and constraints. Then, we provide an overview of the “Defense-in-Depth” model that we adapted for securing ITS vehicle. The proposed security framework follows a modular design and is application oriented. To illustrate the interaction between designed CIVAS layers, we consider a vehicle stationary as example of a road safety use case.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"4 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":"125388336","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.2014.6856613
Jan Oberländer, Sebastian Klemm, Marc Essinger, A. Rönnau, T. Schamm, Johann Marius Zöllner, R. Dillmann
As intelligent vehicles become more and more capable, they must learn to navigate and localize themselves in a wide variety of environments, including GPS-denied and only crudely mapped areas. We argue that since autonomous vehicles must be able to perceive, and semantically interpret, their immediate environment, they should be able to use abstract semantic information as their sole means of localization. This simplifies the level of detail and precision required from environment maps so that, for example, a rough floor plan of a parking garage will suffice to autonomously navigate it. We propose a concept for semantic localization which only requires a conceptual semantic map of the environment, and can be made to work with any kind of sensor data from which the required semantic information can be extracted. We present a localization algorithm which may be used as a base for semantic navigation, e.g. in context of automated driving, and some initial results of its application in a parking garage scenario.
{"title":"A semantic approach to sensor-independent vehicle localization","authors":"Jan Oberländer, Sebastian Klemm, Marc Essinger, A. Rönnau, T. Schamm, Johann Marius Zöllner, R. Dillmann","doi":"10.1109/IVS.2014.6856613","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856613","url":null,"abstract":"As intelligent vehicles become more and more capable, they must learn to navigate and localize themselves in a wide variety of environments, including GPS-denied and only crudely mapped areas. We argue that since autonomous vehicles must be able to perceive, and semantically interpret, their immediate environment, they should be able to use abstract semantic information as their sole means of localization. This simplifies the level of detail and precision required from environment maps so that, for example, a rough floor plan of a parking garage will suffice to autonomously navigate it. We propose a concept for semantic localization which only requires a conceptual semantic map of the environment, and can be made to work with any kind of sensor data from which the required semantic information can be extracted. We present a localization algorithm which may be used as a base for semantic navigation, e.g. in context of automated driving, and some initial results of its application in a parking garage scenario.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"18 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":"125475789","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.2014.6856557
Fredrik Sandblom, Joakim Sorstedt
This paper presents a fusion architecture designed for vehicle manufacturers that use multiple sensor systems to realize several active safety applications, ranging from standard systems to autonomous systems. Advantages and disadvantages of design choices are discussed and the methods we have chosen to implement in two demonstrator vehicles are used as examples. The main contributions are the separation of the architecture into three categories, the state vector parametrization, and the use of multiple output-lists. Together, these choices make it possible to re-use verified core functionality while filters can be independently tuned to meet application-specific requirements.
{"title":"Sensor data fusion for multiple configurations","authors":"Fredrik Sandblom, Joakim Sorstedt","doi":"10.1109/IVS.2014.6856557","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856557","url":null,"abstract":"This paper presents a fusion architecture designed for vehicle manufacturers that use multiple sensor systems to realize several active safety applications, ranging from standard systems to autonomous systems. Advantages and disadvantages of design choices are discussed and the methods we have chosen to implement in two demonstrator vehicles are used as examples. The main contributions are the separation of the architecture into three categories, the state vector parametrization, and the use of multiple output-lists. Together, these choices make it possible to re-use verified core functionality while filters can be independently tuned to meet application-specific requirements.","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":"123040039","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.2014.6856544
Kristoffer Tagesson, B. Jacobson, L. Laine
Front tyre blow-outs lead to several fatal accidents involving heavy vehicles. Common for most heavy vehicles is a positive scrub radius. This can result in a destabilizing steering wheel torque at front tyre blow-out. In this study the safety improvement achieved when reducing scrub radius is quantified. By using a heavy truck equipped with a modified electric power steering system it was possible to change the scrub radius virtually. Brakes were configured to emulate front tyre blow-out which appeared as a sudden disturbance on one of the front tyres. In total 20 drivers took part in the study which was run on a test track at 50 km/h. Results show that the produced average lateral deviation from the original direction was 23 cm, when scrub radius was 12 cm, compared to 16 cm, when scrub radius was 0 cm. The main cause of the observed difference was a small, yet significant, initial overshoot in steering wheel angle which can be derived from the destabilizing steering wheel torque.
{"title":"Driver response at tyre blow-out in heavy vehicles & the importance of scrub radius","authors":"Kristoffer Tagesson, B. Jacobson, L. Laine","doi":"10.1109/IVS.2014.6856544","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856544","url":null,"abstract":"Front tyre blow-outs lead to several fatal accidents involving heavy vehicles. Common for most heavy vehicles is a positive scrub radius. This can result in a destabilizing steering wheel torque at front tyre blow-out. In this study the safety improvement achieved when reducing scrub radius is quantified. By using a heavy truck equipped with a modified electric power steering system it was possible to change the scrub radius virtually. Brakes were configured to emulate front tyre blow-out which appeared as a sudden disturbance on one of the front tyres. In total 20 drivers took part in the study which was run on a test track at 50 km/h. Results show that the produced average lateral deviation from the original direction was 23 cm, when scrub radius was 12 cm, compared to 16 cm, when scrub radius was 0 cm. The main cause of the observed difference was a small, yet significant, initial overshoot in steering wheel angle which can be derived from the destabilizing steering wheel torque.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"4 6 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":"123061206","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.2014.6856596
Keisuke Yoneda, Hossein Tehrani Niknejad, T. Ogawa, Naohisa Hukuyama, S. Mita
In recent years, automated vehicle researches move on to the next stage, that is auto-driving experiments on public roads. Major challenge is how to robustly drive at complicated situations such as narrow or non-featured road. In order to realize practical performance, some static information should be kept on memory such as road topology, building shape, white line, curb, traffic light and so on. Currently, some measurement companies have already begun to prepare map database for automated vehicles. They are able to provide highly-precise 3-D map for robust automated driving. This study focuses on what kind of data should be observed during automated driving with such precise database. In particular, we focus on the accurate localization based on the use of lidar data and precise 3-D map, and propose a feature quantity for scan data based on distribution of clusters. Localization experiment shows that our method can measure surrounding uncertainty and guarantee accurate localization.
{"title":"Lidar scan feature for localization with highly precise 3-D map","authors":"Keisuke Yoneda, Hossein Tehrani Niknejad, T. Ogawa, Naohisa Hukuyama, S. Mita","doi":"10.1109/IVS.2014.6856596","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856596","url":null,"abstract":"In recent years, automated vehicle researches move on to the next stage, that is auto-driving experiments on public roads. Major challenge is how to robustly drive at complicated situations such as narrow or non-featured road. In order to realize practical performance, some static information should be kept on memory such as road topology, building shape, white line, curb, traffic light and so on. Currently, some measurement companies have already begun to prepare map database for automated vehicles. They are able to provide highly-precise 3-D map for robust automated driving. This study focuses on what kind of data should be observed during automated driving with such precise database. In particular, we focus on the accurate localization based on the use of lidar data and precise 3-D map, and propose a feature quantity for scan data based on distribution of clusters. Localization experiment shows that our method can measure surrounding uncertainty and guarantee accurate localization.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"28 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":"126566214","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.2014.6856454
P. Shinzato, D. Wolf, C. Stiller
Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a robust sensor fusion-based method capable of detecting obstacles in a wide variety of scenarios using a minimum number of parameters. Our approach is based on the spatial-relationship on perspective images provided by a single camera and a 3D LIDAR. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results.
{"title":"Road terrain detection: Avoiding common obstacle detection assumptions using sensor fusion","authors":"P. Shinzato, D. Wolf, C. Stiller","doi":"10.1109/IVS.2014.6856454","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856454","url":null,"abstract":"Obstacle detection is a fundamental task for Advanced Driver Assistance Systems (ADAS) and Self-driving cars. Several commercial systems like Adaptive Cruise Controls and Collision Warning Systems depend on them to notify the driver about a risky situation. Several approaches have been presented in the literature in the last years. However, most of them are limited to specific scenarios and restricted conditions. In this paper we propose a robust sensor fusion-based method capable of detecting obstacles in a wide variety of scenarios using a minimum number of parameters. Our approach is based on the spatial-relationship on perspective images provided by a single camera and a 3D LIDAR. Experimental tests have been carried out in different conditions using the standard ROAD-KITTI benchmark, obtaining positive results.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"18 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":"116630964","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.2014.6856492
M. Boumediene, Jean-Philippe Lauffenburger, Jérémie Daniel, C. Cudel
This paper tackles the problem of tracking-based Traffic Sign Recognition (TSR) systems. It presents an integrated object detection, association and tracking approach based on a spatio-temporal data fusion. This algorithm tracks detected sign candidates in order to reduce false positives. Regions Of Interest (ROIs) potentially containing traffic signs are determined from the vehicle-mounted camera images. An original corner detector associated to pixel coding ensures the detection efficiency. The ROIs are combined using the Transferable Belief Model semantics. The associations maximizing the pairwise belief between the detected ROIs and ROIs tracked by multiple Kalman filters are processed. The track evolution helps to detect false positives. Thanks to this solution and to a feedback loop between the tracking algorithm and the ROI detector, a false positive reduction of 45% is assessed.
{"title":"Coupled detection, association and tracking for Traffic Sign Recognition","authors":"M. Boumediene, Jean-Philippe Lauffenburger, Jérémie Daniel, C. Cudel","doi":"10.1109/IVS.2014.6856492","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856492","url":null,"abstract":"This paper tackles the problem of tracking-based Traffic Sign Recognition (TSR) systems. It presents an integrated object detection, association and tracking approach based on a spatio-temporal data fusion. This algorithm tracks detected sign candidates in order to reduce false positives. Regions Of Interest (ROIs) potentially containing traffic signs are determined from the vehicle-mounted camera images. An original corner detector associated to pixel coding ensures the detection efficiency. The ROIs are combined using the Transferable Belief Model semantics. The associations maximizing the pairwise belief between the detected ROIs and ROIs tracked by multiple Kalman filters are processed. The track evolution helps to detect false positives. Thanks to this solution and to a feedback loop between the tracking algorithm and the ROI detector, a false positive reduction of 45% is assessed.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"25 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":"125184126","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.2014.6856583
A. C. C. Pinilla, Christian G. Quintero, Chinthaka Premachandra
This paper considers the problem of diagnosing people's driving skills under real driving conditions using GPS data and video records. For this real environment implementation, a brand new intelligent driving diagnosis system based on fuzzy logic was developed. This system seeks to propose an abstraction of expert driving criteria for driving assessment. The analysis takes into account GPS signals such as: position, velocity, accelerations and vehicle yaw angle; because of its relation with drivers' maneuvers. In that sense, this work presents in the first place, the proposed scheme for the intelligent driving diagnosis agent in terms of its own characteristics properties, which explain important considerations about how an intelligent agent must be conceived. Secondly, it attempts to explain the scheme for the implementation of the intelligent driving diagnosis agent based on its fuzzy logic algorithm, which takes into account the analysis of real-time telemetry signals and proposed set of driving diagnosis rules for the intelligent driving diagnosis, based on a quantitative abstraction of some traffic laws and some secure driving techniques. Experimental testing has been performed in driving conditions. All tested drivers performed the driving task on real streets. The testing results show that our intelligent driving diagnosis system allows quantitative qualifications of driving performance with a high degree of reliability.
{"title":"Intelligent driving diagnosis based on a fuzzy logic approach in a real environment implementation","authors":"A. C. C. Pinilla, Christian G. Quintero, Chinthaka Premachandra","doi":"10.1109/IVS.2014.6856583","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856583","url":null,"abstract":"This paper considers the problem of diagnosing people's driving skills under real driving conditions using GPS data and video records. For this real environment implementation, a brand new intelligent driving diagnosis system based on fuzzy logic was developed. This system seeks to propose an abstraction of expert driving criteria for driving assessment. The analysis takes into account GPS signals such as: position, velocity, accelerations and vehicle yaw angle; because of its relation with drivers' maneuvers. In that sense, this work presents in the first place, the proposed scheme for the intelligent driving diagnosis agent in terms of its own characteristics properties, which explain important considerations about how an intelligent agent must be conceived. Secondly, it attempts to explain the scheme for the implementation of the intelligent driving diagnosis agent based on its fuzzy logic algorithm, which takes into account the analysis of real-time telemetry signals and proposed set of driving diagnosis rules for the intelligent driving diagnosis, based on a quantitative abstraction of some traffic laws and some secure driving techniques. Experimental testing has been performed in driving conditions. All tested drivers performed the driving task on real streets. The testing results show that our intelligent driving diagnosis system allows quantitative qualifications of driving performance with a high degree of reliability.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"74 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":"125228999","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.2014.6856524
M. Lundgren, Erik Stenborg, L. Svensson, Lars Hammarstrand
In the research on autonomous vehicles, self-localization is an important problem to solve. In this paper we present a localization algorithm based on a map and a set of off-the-shelf sensors, with the purpose of evaluating this low-cost solution with respect to localization performance. The used test vehicle is equipped with a Global Positioning System receiver, a gyroscope, wheel speed sensors, a camera providing information about lane markings, and a radar detecting landmarks along the road. Evaluation shows that the localization result is within or close to the requirements for autonomous driving when lane markers and good radar landmarks are present. However, it also indicates that the solution is not robust enough to handle situations when one of these information sources is absent.
{"title":"Vehicle self-localization using off-the-shelf sensors and a detailed map","authors":"M. Lundgren, Erik Stenborg, L. Svensson, Lars Hammarstrand","doi":"10.1109/IVS.2014.6856524","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856524","url":null,"abstract":"In the research on autonomous vehicles, self-localization is an important problem to solve. In this paper we present a localization algorithm based on a map and a set of off-the-shelf sensors, with the purpose of evaluating this low-cost solution with respect to localization performance. The used test vehicle is equipped with a Global Positioning System receiver, a gyroscope, wheel speed sensors, a camera providing information about lane markings, and a radar detecting landmarks along the road. Evaluation shows that the localization result is within or close to the requirements for autonomous driving when lane markers and good radar landmarks are present. However, it also indicates that the solution is not robust enough to handle situations when one of these information sources is absent.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"112 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":"128069184","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.2014.6856389
Mohinder Pandey, Y. Dalbah
This paper proposes a method for reducing false warnings at road intersections, which uses a novel multi model rule based approach for long term trajectory predictions (1s-3s) and incorporates road crossing angle as additional input from the environment. Two major contributions to the field of collision warning have resulted from this research. First, an 8-state variable vehicle motion model is expanded from CTRA model and incorporates yaw-acceleration and acceleration-gradient as additional input variables. Second, a set of motion models are selected based on vehicle maneuver mode and turning phases. The crucial component is the incorporating road crossing angle in the switching logic. The system is tested using simulated and real world data and is shown to reduce false warnings by reducing long term trajectory prediction errors.
{"title":"A method for reducing false warnings in collision warning system (CWS) during turning maneuvers at road intersection","authors":"Mohinder Pandey, Y. Dalbah","doi":"10.1109/IVS.2014.6856389","DOIUrl":"https://doi.org/10.1109/IVS.2014.6856389","url":null,"abstract":"This paper proposes a method for reducing false warnings at road intersections, which uses a novel multi model rule based approach for long term trajectory predictions (1s-3s) and incorporates road crossing angle as additional input from the environment. Two major contributions to the field of collision warning have resulted from this research. First, an 8-state variable vehicle motion model is expanded from CTRA model and incorporates yaw-acceleration and acceleration-gradient as additional input variables. Second, a set of motion models are selected based on vehicle maneuver mode and turning phases. The crucial component is the incorporating road crossing angle in the switching logic. The system is tested using simulated and real world data and is shown to reduce false warnings by reducing long term trajectory prediction errors.","PeriodicalId":254500,"journal":{"name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","volume":"84 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":"133293833","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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