PHD THESIS DRIVING ENVIRONMENT PERCEPTION FROM STEREOVISION. Eng. Florin Ioan ONIGA. Advisor, Prof.dr.ing. Sergiu NEDEVSCHI.

Transcription

1 FACULTATEA DE AUTOMATICĂ ŞI CALCULATOARE Eng. Florin Ioan ONIGA PHD THESIS abstract DRIVING ENVIRONMENT PERCEPTION FROM STEREOVISION Advisor, Prof.dr.ing. Sergiu NEDEVSCHI 2011

2 ABSTRACT Stereovision-based perception of the driving environment is an important research topic in the field of Advanced Driving Assistance Systems (ADAS). There are many open problems in this field, and this thesis was aimed to provide solutions: increasing the accuracy of the 3D reconstruction, providing the appropriate scene representation, modeling and detection of the road surface and obstacles, and modeling and detection of curbs. The research objectives of this thesis are manifold, aiming to find original solutions for improving the stereovision-based perception of traffic scenes. The first objective was to improve the accuracy of 3D reconstruction with sparse stereovision in the context of ADAS for highway driving. The insufficient depth range provided by conventional stereovision techniques, considering the high speeds encountered, motivated the need for improved accuracy. Another important objective was to provide a more appropriate representation of structured and unstructured driving environments and means to extract this representation from the dense stereovision sensorial data. Secondary objectives, related to the environment representation, were derived. A more general road model and real-time means of road computation were needed. Special urban scene features such as traffic isles and curbs need original dedicated models and detection approaches. Improving the obstacle-road separation for the higher-level modules was an additional objective. This thesis is structured as follows: Chapter 1 presents the motivation and the main objectives of the research work. Chapter 2 presents a short overview of various sensors, passive or active, which are used for low-level perception in driving assistance applications. This provides a brief insight of how each sensor works, what data it provides, and what the challenges are for the higher level processing algorithms. We present an overview of the standard sparse stereovision approach, suitable for realtime implementation on standard processors. The drawbacks of the standard stereo matching method and the sub-pixel accuracy are discussed. Chapter 3 presents a survey of the most relevant road and obstacle detection approaches based on stereovision. A comparison of the existing road models and the processing space (the 3D data representation used for processing) is presented, underlining the limitations of existing techniques. Then, the main existing approaches are briefly discussed. Chapter 4 presents the state of the art in curb detection for advanced driving assistance systems. Due to the small number of papers on curb detection from stereovision, we also present curb detection methods based on other sensors. Existing approaches for curb detection are analyzed by considering two criteria: the type of sensor used and the curb model proposed. The main techniques for fitting a parametric model to a data set are briefly discussed. Additionally, we present the detailed formalism and a numerical analysis for the Least SQuares (LSQ) fitting of a line with vertical and perpendicular residuals. This was needed in order to justify our choice later throughout this thesis for the LSQ approach with vertical offsets that provides enough accuracy and has a much lower complexity. Chapter 5 presents a novel approach for sparse stereovision with increased accuracy. The standard stereo matching approach uses left and right pixel accuracy features and derives the sub-pixel accuracy by interpolating the similarity score. The proposed approach recovers the sub-pixel accuracy features in both images before the matching is performed. Correspondences are computed between the sub-pixel accuracy edges, with increased precision. Original solutions are presented for analyzing and improving the accuracy of sub-pixel edge detection, contours segmentation based on corner detection from the sub-pixel contour profile, and a contour-based stereo matching scheme. The increased accuracy can prove valuable for applications that require an accurate 3D reconstruction and description of environment. The evaluation of the proposed approach shows that an accurate 3D environment reconstruction, up to larger distances, is achieved. Chapter 6 presents the original solutions proposed for the perception of traffic scenes based on stereovision, and the Digital Elevation Map (DEM) representation. The proposed environment representation and detection approaches were developed in the context of dense stereovision. A general representation model for structured and unstructured traffic scenes was presented as 2

3 an advanced occupancy map that provides information about the type of each cell (drivable area, obstacles, and traffic isles). The proposed representation of obstacles and traffic isles as cell blobs instead of cuboids is more flexible (closer to the real 3D shape). Two original complementary road models were presented: a global parametric road surface model consisting of a high-order polynomial surface that is able to model complex vertical road profiles, and a local parametric density-based road model that can virtually model any road shape as long as the local road slope is below a threshold. A real-time original method for computing the polynomial road surface from a DEM was proposed. The method combines statistical matching, LSQ minimization and region growing supervised by the stereo sensor model. A real-time method was proposed for computing the local parametric road surface based on the density of 3D points in the DEM, by comparing the actual density with the expected road density derived from the stereo sensor model. For each road model, we proposed methods to classify the DEM cells based on geometric constraints and the stereo sensor model. In order to improve the robustness, we proposed fusion of the two classification results and a method for DEM temporal filtering based on ego-motion compensation and persistence evaluation. A real-time algorithm was developed based on these contributions. It takes as input dense 3D points, thus road edge features (high gradient) are not required. The 3D set of points is transformed into a digital elevation map in order to achieve real-time processing. The global road model allows complex vertical road shapes, often found in urban scenarios, and takes into account the 3D uncertainty increasing with the depth. Possible lack of road dense 3D data, due to poor road texture, is compensated by the density-based road model method. The tests performed proved the robustness of the algorithm. Another contribution presented is the mixed parametric road surface model consisting of two joint surfaces: a high-order (quadratic) polynomial surface for close-range road modeling and a loworder (planar) surface for far-range road modeling. This model improves the obstacle-road separation in the context of crowded scenes, where most of the road is occluded, uphill/downhill roads, or when the road texture cannot be reconstructed in a dense manner. Several contributions were presented with the goal of enhancing the environment representation proposed. Traffic isles are filtered based on their temporal persistence, in order to remove false positives. Obstacles are classified based on their temporal persistency as static or dynamic. This extends the representation with two new classes for the DEM cells. A global map is built by taking into account the ego motion and by integrating the occupancy grid over time. The global map can be used for alignment with offline maps of the scene. Chapter 7 presents the original solutions related to the problem of curb detection based on stereovision. Multiple curb detection approaches were proposed in this chapter, which are able to detect curbs in real-time based on the DEM representation using original curb models. The first approach relies on the existing linear curb model but it performs the whole processing on the DEM, instead of the image-disparity space as the existing methods do. The method extracts the relevant linear curbs by using the Hough transform applied to the set of DEM edges, and then it identifies the curb segments along the lines. The main benefit of the proposed approach is a low computational complexity. The second approach uses an original poly-linear curb model that is able to model curved curbs. In order to filter false curb points, a multi-frame persistence map was proposed. The dominant curb segment is detected with the Hough transform. The segment is extended iteratively with adjacent segments by analyzing the local elevations. Each segment has its own vertical linear profile computed with a RANSAC approach. The third approach is based on an original cubic polynomial curb model that allows accurate modeling of curbs having curvature and curvature variation. False curb points are filtered with temporal and occlusion constraints. In order to fit the polynomial model, a modified RANSAC approach was proposed: bad samples are rejected at an early phase, and the consensus score is computed in a better way that ensures safe fitting of the polynomial. The vertical profile of the curb is modeled with a quadratic polynomial, computed also with RANSAC. Finally, the fourth approach uses an original curb model consisting of a cubic spline/piecewise- 3

4 cubic polynomial curve. This model was required for modeling curbs over large distances. An original iterative fitting scheme was proposed for the piecewise polynomial curve consisting of LSQ fitting on each interval and continuity/smoothness constraints (first order differentiable curve in the knots). A good support for fitting was provided by temporal integration of curb points along consecutive frames. A modified Iterative Closest Point approach applied to the curb elevation data is proposed for vertical alignment between consecutive frames. The evaluation of the proposed approach proved the benefits of the general model proposed: better modeling and increased stability between frames. Chapter 8 concludes this thesis. Next, we underline the most important contributions of this thesis: 1. Design and development of an increased accuracy 3D reconstruction system based on the matching of sub-pixel accuracy contours, suitable for highway advanced driving assistance systems: Using the general geometry (no image rectification) for stereo reconstruction. A better 3D accuracy is obtained by avoiding image re-sampling due to the rectification step. A novel technique for sub-pixel stereo matching by computing the correspondent as the intersection between the epipolar line and the sub-pixel contour from the right image. 2. Design and development of an original representation of complex traffic scenes, and real-time processing methods to generate the representation based on Digital Elevation Maps: General representation modeling of traffic scenes with an advanced occupancy map that provides information about the type of each cell (drivable area, static / dynamic obstacles, and traffic isles). A global parametric road surface model consisting of a high-order polynomial surface that is able to model complex vertical road profiles. Real-time method for computing the parametric road surface by fitting a high-order polynomial surface to a digital elevation map. The method combines statistical matching, LSQ minimization and region growing supervised by the stereo sensor model. 3. Design and development of multiple original curb detection algorithms, based on Digital Elevation Maps processing. Various original curb models and real-time fitting techniques were proposed: The representation of curbs using a cubic polynomial curb model, which allows modeling of curbs having with constant curvature variation. Each curb has a vertical profile modeled with a quadratic polynomial. The general representation of curbs with a cubic spline / piecewise-cubic polynomial model, which allows modeling curbs on larger areas around the ego vehicle. Method for real-time fitting of the cubic polynomial curb model to a DEM, based on a RANSAC approach with early rejection of bad samples and a new way of computing the consensus set. Method for real-time fitting of the piecewise-cubic polynomial curb model to a DEM, based on iterative LSQ fitting on each interval, constraining the curve to be smooth (first order differentiable). The scientific impact of this thesis is proven by the published papers (ISI rated journals, ISI proceedings conferences, BDI) and the relevant number of independent citations for the papers that present in detail the contributions of this thesis. Published papers In ISI rated international journals 1. F. Oniga, S. Nedevschi, Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection, IEEE Transactions on Vehicular Technology, Vol. 59, Issue 3, 2010, pp , ISSN: ISI impact factor (2010), (5-year). 2. R. Danescu, F. Oniga, S. Nedevschi, Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid, IEEE Transactions on Intelligent Transportation Systems, 12, pp. 1-12, ISI impact factor (2010), (5-year). 4

5 3. S. Nedevschi, V. Popescu, T. Marita, R. Danescu, F. Oniga, Alignment of Sensor Data and Digital Maps for Enhanced Intersection Environment Perception, submitted for publication to Machine Vision and Applications (currently under review) (ISI) In ISI Proceedings (mostly) / IEEE Xplore / SpringerLink 4. F. Oniga, S. Nedevschi, Curb Detection for Driving Assistance Systems: A Cubic Spline-Based Approach, Proc. of IEEE Intelligent Vehicles Symposium (IV), 5-9 June 2011, Baden-Baden, Germany, pp F. Oniga, M. Miron, R. Danescu, S. Nedevschi, Automatic Recognition of Low Earth Orbit Objects from Image Sequences, Proc. of the IEEE International Conference on Intelligent Computer Communication and Processing, aug. 2011, Cluj-Napoca, Romania. 6. A. Vatavu, S. Nedevschi, F. Oniga, Real time environment representation in driving scenarios based on object delimiters extraction, Lecture Notes in Electrical Engineering 85 LNEE, pp , F. Oniga, S. Nedevschi, Polynomial Curb Detection Based on Dense Stereovision for Driving Assistance, Proc. of the 13th International IEEE Conference on Intelligent Transportation Systems, Sept. 2010, Madeira, Portugal, ISBN: F. Oniga, R. Danescu, S. Nedevschi, Mixed Road Surface Model for Driving Assistance Systems, Proc. of the IEEE International Conference on Intelligent Computer Communication and Processing, aug. 2010, Cluj-Napoca, Romania, pp , ISBN: A. Vatavu, S. Nedevschi, F. Oniga, Real-time environment representation based on Occupancy Grid temporal analysis using a Dense Stereo-Vision System, Proc. of the IEEE International Conference on Intelligent Computer Communication and Processing, aug. 2010, Cluj-Napoca, Romania, pp , ISBN: I. Haller, C. Pantilie, F. Oniga, and S. Nedevschi, "Real-time semi-global dense stereo solution with improved subpixel accuracy, Proc. of IEEE Intelligent Vehicles Symposium (IV), San Diego, CA, 2010, pp , ISBN: R. Danescu, F. Oniga, S. Nedevschi, Particle Grid Tracking System for Stereovision Based Environment Perception, Proc. of IEEE Intelligent Vehicles Symposium (IV), San Diego, CA, 2010, pp , ISBN: S. Nedevschi, T. Marita, R. Danescu, F. Oniga, S. Bota, I. Haller, C. Pantilie, M. Drulea, C. Golban, On-board 6D Visual Sensor for Intersection Driving Assistance, book chapter in Advanced Microsystems for Automotive Applications, part 4, 2010, Springer, ISBN: , pp R. Danescu, F. Oniga, S. Nedevschi, M-M. Meinecke, Tracking Multiple Objects Using Particle Filters and Digital Elevation Maps, in Proc. of the IEEE Intelligent Vehicle Symposium (IEEE-IV 2009), June 2009, Xi An, China, pp R. Danescu, D. Lebu, F. Oniga, S. Nedevschi, M.-M. Meinecke, A Flexible Solution for Detection and Tracking of Multiple Objects, in proc. of IEEE International Conference on Intelligent Computer Communication and Processing 2009 (ICCP 2009), Cluj-Napoca, Romania, pp , ISBN F. Oniga, S. Nedevschi, R. Danescu, M.-M. Meinecke, Global Map Building Based on Occupancy Grids Detected from Dense Stereo in Urban Environments, in proc. of IEEE International Conference on Intelligent Computer Communication and Processing 2009 (ICCP 2009), Cluj-Napoca, Romania, pp ISBN S. Nedevschi, T. Marita, R. Danescu, F. Oniga, S. Bota, On-board Stereo Sensor for Intersection Driving Assistance. Architecture and Specification, in proc. of IEEE International Conference on Intelligent Computer Communication and Processing 2009 (ICCP 2009), Cluj-Napoca, Romania, pp , ISBN S. Nedevschi, R. Danescu, T. Marita, F. Oniga, C. Pocol, S. Bota, M-M. Meinecke, M. A. Obojski, Stereovision- Based Sensor for Intersection Assistance, book chapter in Advanced Microsystems for Automotive Applications, April 2009, Springer, ISBN , pp A. Vatavu, S. Nedevschi, F. Oniga, Real Time Object Delimiters Extraction for Environment Representation in Driving Scenarios, ICINCO-RA 2009: F. Oniga, S. Nedevschi, M-M. Meinecke, Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Oct. 2008, Beijing, China, pp F. Oniga, S. Nedevschi, M-M. Meinecke, Curb Segments Detection with Temporal Filtering for Urban Driving, Proceedings of 4th International IEEE Conference on Intelligent Computer Communication and Processing, Aug. 2008, Cluj-Napoca, Romania, pp , ISBN S. Nedevschi, A. Vatavu, F. Oniga, Forward Collision Detection using a Stereo Vision System, Proceedings of 4th International IEEE Conference on Intelligent Computer Communication and Processing, Aug. 2008, Cluj- Napoca, Romania, pp , ISBN F. Oniga, S. Nedevschi, M-M. Meinecke, T-B. To, Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo, Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems, Sept Oct. 3, 2007, Seattle, Washington, USA, ISBN:

6 23. F. Oniga, S. Nedevschi, M-M. Meinecke, Curb Detection Based on Elevation Maps from Dense Stereo, Proceedings of 3rd International IEEE Conference on Intelligent Computer Communication and Processing, pp , 6-8 Sept. 2007, Cluj-Napoca, Romania, ISBN T. Marita, F. Oniga, S. Nedevschi, T. Graf, Calibration Accuracy Assessment Methods for Stereovision Sensors Used in Vehicles, Proceedings of 3rd International IEEE Conference on Intelligent Computer Communication and Processing, 6-8 Sept. 2007, Cluj-Napoca, Romania, pp , ISBN S. Nedevschi, R. Danescu, T. Marita, F. Oniga, C. Pocol, S. Sobol, C. Tomiuc, C. Vancea, M.M. Meinecke, T. Graf, T. B. To, M.A. Obojski, A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision, Proceedings of 2007 IEEE Intelligent Vehicles Symposium, (IV2007), Istanbul, Turkey, June 13-15, 2006, pp , ISBN / T. Marita, F. Oniga, S. Nedevschi, T. Graf, R. Schmidt, Camera Calibration Method for Far Range Stereovision Sensors Used in Vehicles, Proceedings of IEEE Intelligent Vehicles Symposium, (IV2006), June 13-15, 2006, Tokyo, Japan, pp , ISBN X. 27. S. Nedevschi, F. Oniga, R. Danescu, T. Graf, R. Schmidt, Increased Accuracy Stereo Approach for 3D Lane Detection, Proceedings of IEEE Intelligent Vehicles Symposium, (IV2006), June 13-15, 2006, Tokyo, Japan, pp , ISBN X. 28. S. Nedevschi, S. Bota, T. Marita, F. Oniga, C. Pocol, Real-Time 3D Environment Reconstruction Using High Precision Trinocular Stereovision, 2006 IEEE-TTTC International Conference on Automation, Quality&Testing, Robotics AQTR 2006 (THETA 15), May Cluj-Napoca, Romania, ISBN / S. Nedevschi, R. Danescu, T. Marita, F. Oniga, C. Pocol, S. Sobol, T. Graf, R. Schmidt, Driving Environment Perception Using Stereovision, Proceedings of IEEE Intelligent Vehicles Symposium, (IV2005), June 2005, Las Vegas, USA, pp , ISBN / S. Nedevschi, R..Schmidt, T. Graf, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, 3D Lane Detection System Based on Stereovision, IEEE Intelligent Transportation Systems Conference (ITSC), October 2004, Washington, USA, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, R. Schmidt, T. Graf, High Accuracy Stereo Vision System for Far Distance Obstacle Detection, IEEE Intelligent Vehicles Symposium, (IV2004), June 2004, Parma, Italy, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, Spatial Grouping of 3D Points from Multiple Stereovision Sensors, IEEE International Conference of Networking, Sensing and Control, March 2004, Taipei, Taiwan, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, Online detection of dynamic traffic description using stereo sensor, GPS and wireless communication equipped vehicles, IIIS International Conference on Computing Communication and Control Technologies, August 2004, Austin, Texas, pp , ISBN S. Nedevschi, T. Marita, R. Danescu, D. Frentiu, F. Oniga, C. Pocol., Real-Time Extraction of 3D Dynamic Environment Description Using Multiple Stereovision Sensors, Proceedings of International Conference on CCCT 2003, Orlando, Florida, 29 July 1 August, 2003, Volume 3, pp In ISI indexed international journals 35. S. Nedevschi, T. Marita, M. Vaida, R. Danescu, D. Frentiu, F. Oniga, C. Pocol Camera Calibration Method for Stereo Measurements, Journal of Control Engineering and Applied Informatics (CEAI), Vol.4, No. 2, pp.21-28, 2002, Bucuresti, Romania. In the proceedings of other international conferences 36. F. Oniga, S. Nedevschi, M. M. Meinecke, Temporal Integration of Occupancy Grids Detected from Dense Stereo Using an Elevation Map Representation, in Proceedings of the 6th International Workshop on Intelligent Transportation (WIT 2009), Hamburg, Germany, pp S. Nedevschi, A. Vătavu, F. Oniga, Forward Collision Detection based on Elevation Map from Dense Stereo, in Proceedings of the IROS nd Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Nice, France; pp F. Oniga, S. Nedevschi, Improving the Accuracy of 3D Stereo Reconstruction through Sub-Pixel Contour-Based Matching, in Proceedings of IEEE 2-nd International Conference on Intelligent Computer Communication and Processing, 1-2 Sept. 2006, Cluj-Napoca, Romania, pp , ISBN (10) S. Nedevschi, T. Marita, R. Danescu, F. Oniga, C. Pocol, S. Sobol, C. Tomiuc, C. Vancea, S. Bota, Stereovision Sensor for Driving Assistance, in Proceedings of IEEE 2-nd International Conference on Intelligent Computer Communication and Processing, 1-2 Sept. 2006, Cluj-Napoca, Romania, pp , ISBN (10) S. Nedevschi, R. Danescu, T. Marita, F. Oniga, C. Pocol, Moving Camera Rotation Estimation Using Horizon Line Features Motion Field, in Proceedings of 6-th International Carpathian Control Conference, May 2005, Lilafured-Miskolc, Hungary, pp , ISBN

7 41. S. Nedevschi, T. Marita, R. Danescu, F. Oniga, C. Pocol, Camera Calibration method for high-accuracy sterovision, in Proceedings of the Joint-Hungarian-Austrian Conference on Image Processing and Pattern Recognition, Vesprem, Hungary, May 2005, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, Thorsten Graf, Rolf Schmidt, High Accuracy Stereovision Approach for Obstacle Detection on Non-Planar Roads, IEEE Inteligent Engineering Systems (INES), September 2004, Cluj Napoca, Romania, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, Stereovision Approach For Obstacle Detection On Non-Planar Roads, IEEE and IFAC International Conference on Informatics in Control, Automation and Robotics, August 2004, Setubal, Portugal, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, Dynamic traffic description using stereovision equipped vehicles and ad-hoc wireless networking, IEEE-TTTC International Conference on Automation, Quality Testing and Robotics, May 2004, Cluj Napoca, Romania, pp , ISBN S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, 3D Environment Reconstruction Using Multiple Moving Stereovision Sensors, microcad International Scientific Conference, March 2004, Miskolc, Hungary, pp , ISBN S. Nedevschi, T. Marita, R. Danescu, D. Frentiu, F. Oniga, C. Pocol, Camera Calibration Error Analysis in Stereo Measurements, Proceedings of MicroCAD 2003 International Scientific Conference, Miskolc, 6-7 March 2003, pp S. Nedevschi, T. Marita, M. Vaida, R. Danescu, D. Frentiu, F. Oniga, C. Pocol Camera Calibration Method for Stereo Measurements, presented at the IEEE-TTTC International Conference on Automation, Quality and Testing, Robotics, May 2002, Cluj-Napoca, Romania, short version published in the conference proceedings at pp In national journals, CNCSIS rated, category B 48. S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, Thorsten Graf, Rolf Schmidt, High Accuracy Stereovision Approach for Obstacle Detection on Non-Planar Roads, Journal of Automation, Computers and Applied Mathematics (ACAM), Vol.14, No. 2, 2005, Cluj-Napoca, Romania, pp , ISSN X. Book chapters (international editors) 49. S. Nedevschi, R. Danescu, T. Marita, F. Oniga, C. Pocol, S. Bota and C. Vancea, A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision, book chapter in Stereo Vision editor A. Bhatti, published by InTech Education and Publishing, Vienna, November 2008, ISBN Relevant Citations Many of the papers published presenting in detail the contributions brought by this thesis have relevant independent citations in international journals and conferences. Below is a list of the most relevant citations identified with Google Scholar. 1. S. Nedevschi, F. Oniga, R. Danescu, T. Graf, R. Schmidt, Increased Accuracy Stereo Approach for 3D Lane Detection, Proceedings of IEEE Intelligent Vehicles Symposium, (IV2006), June 13-15, 2006, Tokyo, Japan, pp , ISBN X. This paper presents the increased accuracy stereo approach (contour-based) presented in chapter 5 3 ISI journal citations and 5 conference proceedings citations 2. F. Oniga, S. Nedevschi, M-M. Meinecke, T-B. To, Road Surface and Obstacle Detection Based on Elevation Maps from Dense Stereo, Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems, Sept Oct. 3, 2007, Seattle, Washington, USA, ISBN: This paper presents the first results for the DEM-based road, obstacle and traffic isle detection approach presented in chapter 6 2 ISI journal citations and 8 conference proceedings citations 3. F. Oniga, S. Nedevschi, Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection, IEEE Transactions on Vehicular Technology, Vol. 59, Issue 3, 2010, pp , ISSN: This paper presents the DEM-based road, obstacle and traffic isle detection approach presented in chapter 6 1 ISI journal citation and one magazine citation 4. F. Oniga, S. Nedevschi, R. Danescu, M.-M. Meinecke, Global Map Building Based on Occupancy Grids Detected 7

8 from Dense Stereo in Urban Environments, in proc. of IEEE International Conference on Intelligent Computer Communication and Processing 2009 (ICCP 2009), Cluj-Napoca, Romania, pp ISBN This paper presents the global map building approach presented in chapter 6 1 international journal citation The next papers present the curb detection algorithms proposed in chapter 7 5. F. Oniga, S. Nedevschi, M-M. Meinecke, Curb Detection Based on Elevation Maps from Dense Stereo, Proceedings of 3rd International IEEE Conference on Intelligent Computer Communication and Processing, pp , 6-8 Sept. 2007, Cluj-Napoca, Romania, ISBN conference proceedings citation 6. F. Oniga, S. Nedevschi, M-M. Meinecke, Curb Detection Based on a Multi-Frame Persistence Map for Urban Driving Scenarios, Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems, Oct. 2008, Beijing, China, pp conference proceedings citations 7. F. Oniga, S. Nedevschi, Curb Detection for Driving Assistance Systems: A Cubic Spline-Based Approach, Proc. of IEEE Intelligent Vehicles Symposium (IV), 5-9 June 2011, Baden-Baden, Germany, pp conference proceedings citation 8. F. Oniga, S. Nedevschi, Polynomial Curb Detection Based on Dense Stereovision for Driving Assistance, Proc. of the 13th International IEEE Conference on Intelligent Transportation Systems, Sept. 2010, Madeira, Portugal, ISBN: conference proceedings citation In addition, I have co-authored other relevant papers with other members of the IPPRG team. These papers usually present whole detection systems based on stereovision and include original contributions from other team members. Below are two of our most cited papers, which present an obstacle detection system for non-planar roads based on sparse stereovision. My contribution to these papers is the sparse stereovision approach using general geometry with standard stereo matching. 9. S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, T. Graf, R. Schmidt, High Accuracy Stereovision Approach for Obstacle Detection on Non-Planar Roads, IEEE Inteligent Engineering Systems (INES), September 2004, Cluj Napoca, Romania, pp , ISBN ISI journal citations and 8 conference proceedings citations 10. S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, R. Schmidt, T. Graf, High Accuracy Stereo Vision System for Far Distance Obstacle Detection, IEEE Intelligent Vehicles Symposium, (IV2004), June 2004, Parma, Italy, pp , ISBN ISI journal citations and 12 conference proceedings citations 8