Isaac Martín de Diego, O. Siordia, C. Conde, E. Cabello
{"title":"Optimal experts' knowledge selection for intelligent driving risk detection systems","authors":"Isaac Martín de Diego, O. Siordia, C. Conde, E. Cabello","doi":"10.1109/IVS.2012.6232208","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232208","url":null,"abstract":"This paper presents a method for the selection of the optimal combination of experts' knowledge needed for the generation of a reliable driving risk ground truth. The driving risk of a controlled driving session, recorded in a highly realistic truck simulator, was evaluated by a large number of traffic safety experts. The risk evaluations were grouped in several clusters in order to find experts with high agreement. Next, a method for the selection of the optimal experts' evaluations is proposed. We found, through the experiments performed in this study, that a low number of experts are sufficient for the properly detection of driving risks. In addition, we show some of the advantages of the consideration of traffic safety experts' knowledge for the generation of a driving risk ground truth.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132023","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}
Kay Weckemann, Florian Satzger, L. Stolz, D. Herrscher, Claudia Linnhoff-Popien
{"title":"Lessons from a minimal middleware for IP-based in-car communication","authors":"Kay Weckemann, Florian Satzger, L. Stolz, D. Herrscher, Claudia Linnhoff-Popien","doi":"10.1109/IVS.2012.6232251","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232251","url":null,"abstract":"Introducing the Internet Protocol to the in-car network, we need communication software for fast and robust development of future networked applications. The heterogeneity within the communication network concerning the differences in device capability and application requirements can be targeted by establishing a middleware consisting of several specifications. In this paper, we give evidence that a minimal middleware specification can be feasible even for smallest embedded Electronic Control Units but still largely interoperable with the more complex communication demands of powerful infotainment and driver assistance ones. We therefore present a prototype implementation, which is evaluated towards interoperability, resource consumption, and execution performance.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115115490","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}
{"title":"An image-to-image loop-closure detection method based on unsupervised landmark extraction","authors":"E. Sariyanidi, O. Sencan, H. Temeltas","doi":"10.1109/IVS.2012.6232174","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232174","url":null,"abstract":"This paper presents a dedicated approach to detect loop closures using visually salient patches. We introduce a novel, energy maximization based saliency detection technique which has been used for unsupervised landmark extraction. We explain how to learn the extracted landmarks on-the-fly and re-identify them. Furthermore, we describe the sparse location representation we use to recognize previously seen locations in order to perform reliable loop closure detection. The performance of our method has been analyzed both on an indoor and an outdoor dataset, and it has been shown that our approach achieves quite promising results on both datasets.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116789043","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}
Scott M. Martin, C. Rose, J. Britt, D. Bevly, Z. Popovic
{"title":"Performance analysis of a scalable navigation solution using vehicle safety sensors","authors":"Scott M. Martin, C. Rose, J. Britt, D. Bevly, Z. Popovic","doi":"10.1109/IVS.2012.6232286","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232286","url":null,"abstract":"GPS receiver performance can suffer in difficult environments such as urban canyons and heavy foliage. Inertial sensors provide information between GPS updates and can enhance the position solution in a GPS/INS architecture. Additional information from safety sensors already on the vehicle, such as lane departure warning (LDW) sensors, can enhance the navigation solution further by constraining inertial errors even in the presence of GPS errors. This paper outlines a scalable navigation solution that can use a combination of GPS, reduced inertial sensors, full inertial data, vehicle CAN data, and vision sensors, depending on what data is available in difficult environments. Data was collected in Detroit, Michigan in a diverse mix of environments that includes heavy foliage, highway, and downtown areas, in proportions representative of what is expected in typical driving. Validation of the approach consists of both a qualitative analysis of the resulting trajectories overlaid on a map of the area and quantitative comparison of the trajectories produced by the proposed system and the reference system.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037203","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}
Guillaume Bresson, T. Féraud, R. Aufrère, P. Checchin, R. Chapuis
{"title":"Parsimonious real time monocular SLAM","authors":"Guillaume Bresson, T. Féraud, R. Aufrère, P. Checchin, R. Chapuis","doi":"10.1109/IVS.2012.6232203","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232203","url":null,"abstract":"This paper presents a real time monocular EKF SLAM process that uses only Cartesian defined landmarks. This representation is easy to handle, light and consequently fast. However, it is prone to linearization errors which can cause the filter to diverge. Here, we will first clearly identify and explain when those problems take place. Then, a solution, able to reduce or avoid the errors involved by the linearization process, will be proposed. Combined with an EKF, our method uses resources parsimoniously by conserving landmarks for a long period of time without requiring many points to be efficient. Our solution is based on a method to properly compute the projection of a 3D uncertainty into the image frame in order to track landmarks efficiently. The second part of this solution relies on a correction of the Kalman gain that reduces the impact of the update when it is incoherent. This approach was applied to a real data set presenting difficult conditions such as severe distortions, reflections, blur or sunshine to illustrate its robustness.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125846886","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}
R. Dang, C. He, Zhang Qiang, Keqiang Li, Yusheng Li
{"title":"ACC of electric vehicles with coordination control of fuel economy and tracking safety","authors":"R. Dang, C. He, Zhang Qiang, Keqiang Li, Yusheng Li","doi":"10.1109/IVS.2012.6232121","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232121","url":null,"abstract":"An adaptive cruise control system of electric vehicles is proposed, considering both fuel economy and tracking safety with model predictive control theory. Firstly, the mathematical relationship between fuel cost and longitudinal acceleration is analyzed through a simulation model. Secondly the 2-norm number is adopted to indicate the integrated cost function, which integrates economy performance and tracking performance together. Finally the proposed optimization problem is solved by model predictive control theory, and a contrast controller is built with linear quadratic algorithm. Both simulation and real vehicle test results show that the MPC controller can reduce fuel cost by above 5% than LQ controller in the range of safe tracking, and it successfully coordinates fuel economy and tracking safety.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123245246","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}
{"title":"Tracking and classification of arbitrary objects with bottom-up/top-down detection","authors":"M. Himmelsbach, Hans-Joachim Wünsche","doi":"10.1109/IVS.2012.6232181","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232181","url":null,"abstract":"Recently, the introduction of dense, long-range 3D sensors has facilitated tracking of arbitrary objects. Especially in the context of autonomous driving, other traffic participants driving the streets usually stay well-segmented from each other. In contrast, pedestrians or bicyclists do not always stay on the road and they often get close to static structure of the environment, e.g. traffic lights or signs, bushes, parking cars etc. These objects are not as easy to segment, often resulting in an under-segmentation of the scene and wrong tracking results. This paper addresses the problem of tracking moving objects that are hard to segment from their static surroundings by utilizing top-down knowledge about the geometry of existing tracks during segmentation. This includes methods for discerning static from moving objects to reduce the rate of false positive tracks as well as a classification of tracks into pedestrian, bicyclist, motor bike, passenger car, van and truck classes by considering an objects appearance and motion history. The proposed tracking system is experimentally validated in challenging real-world inner-city traffic scenes.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127041934","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}
{"title":"Risk assessment at road intersections: Comparing intention and expectation","authors":"S. Lefèvre, C. Laugier, J. Guzman","doi":"10.1109/IVS.2012.6232198","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232198","url":null,"abstract":"Intersections are the most complex and hazardous areas of the road network, and 89% of accidents at intersection are caused by driver error. We focus on these accidents and propose a novel approach to risk assessment: in this work dangerous situations are identified by detecting conflicts between intention and expectation, i.e. between what drivers intend to do and what is expected of them. Our approach is formulated as a Bayesian inference problem where intention and expectation are estimated jointly for the vehicles converging to the same intersection. This work exploits the sharing of information between vehicles using V2V wireless communication links. The proposed solution was validated by field experiments using passenger vehicles. Results show the importance of taking into account interactions between vehicles when modeling intersection situations.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"161 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116549846","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}
{"title":"Vehicle detection and tracking using Mean Shift segmentation on semi-dense disparity maps","authors":"S. Lefebvre, S. Ambellouis","doi":"10.1109/IVS.2012.6232280","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232280","url":null,"abstract":"This paper describes an original joint obstacle detection and tracking method based on a Mean Shift algorithm and semi-dense disparity maps. The semi-dense disparity maps are computed with a local 1D fuzzy scanline stereo matching approach. Each map is associated to a confidence map that is used to remove bad matches. The Mean Shift algorithm is applied to simultaneously extract each vehicle and track the 3D points belonging to the same vehicle along the sequence. We show that several vehicles can be efficiently detected and that a semi-dense disparity map is sufficient to reach an accurate segmentation even when occlusions occur. This paper presents some results on real image sequences acquired in the context of Advanced Driver Assistance Systems.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122671867","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}
Q. Zhu, Long Chen, Qingquan Li, Ming Li, A. Nüchter, Jian Wang
{"title":"3D LIDAR point cloud based intersection recognition for autonomous driving","authors":"Q. Zhu, Long Chen, Qingquan Li, Ming Li, A. Nüchter, Jian Wang","doi":"10.1109/IVS.2012.6232219","DOIUrl":"https://doi.org/10.1109/IVS.2012.6232219","url":null,"abstract":"Finding road intersections in advance is crucial for navigation and path planning of moving autonomous vehicles, especially when there is no position or geographic auxiliary information available. In this paper, we investigate the use of a 3D point cloud based solution for intersection and road segment classification in front of an autonomous vehicle. It is based on the analysis of the features from the designed beam model. First, we build a grid map of the point cloud and clear the cells which belong to other vehicles. Then, the proposed beam model is applied with a specified distance in front of autonomous vehicle. A feature set based on the length distribution of the beam is extracted from the current frame and combined with a trained classifier to solve the road-type classification problem, i.e., segment and intersection. In addition, we also make the distinction between +-shaped and T-shaped intersections. The results are reported over a series of real-world data. A performance of above 80% correct classification is reported at a real-time classification rate of 5 Hz.","PeriodicalId":402389,"journal":{"name":"2012 IEEE Intelligent Vehicles Symposium","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129031454","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}