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Author |
Felipe Codevilla; Eder Santana; Antonio Lopez; Adrien Gaidon |
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Title  |
Exploring the Limitations of Behavior Cloning for Autonomous Driving |
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Conference Article |
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Year |
2019 |
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18th IEEE International Conference on Computer Vision |
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9328-9337 |
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Abstract |
Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, executing complex lateral and longitudinal maneuvers, even in unseen environments, without being explicitly programmed to do so. However, we confirm some limitations of the behavior cloning approach: some well-known limitations (eg, dataset bias and overfitting), new generalization issues (eg, dynamic objects and the lack of a causal modeling), and training instabilities, all requiring further research before behavior cloning can graduate to real-world driving. The code, dataset, benchmark, and agent studied in this paper can be found at github. |
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Seul; Korea; October 2019 |
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ICCV |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ CSL2019 |
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3322 |
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Author |
Arnau Ramisa; Shrihari Vasudevan; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
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Title |
Evaluation of the SIFT Object Recognition Method in Mobile Robots: Frontiers in Artificial Intelligence and Applications |
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Conference Article |
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2009 |
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12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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9-18 |
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General object recognition in mobile robots is of primary importance in order to enhance the representation of the environment that robots will use for their reasoning processes. Therefore, we contribute reduce this gap by evaluating the SIFT Object Recognition method in a challenging dataset, focusing on issues relevant to mobile robotics. Resistance of the method to the robotics working conditions was found, but it was limited mainly to well-textured objects. |
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Cardona, Spain |
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0922-6389 |
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978-1-60750-061-2 |
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CCIA |
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ADAS |
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no |
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Admin @ si @ RVA2009 |
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1248 |
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Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate |
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Title |
Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality |
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Conference Article |
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2013 |
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ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars |
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624-631 |
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Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field. |
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Sydney; Australia; December 2013 |
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CVTT:E2M |
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IAM; ADAS; 600.044; 600.057; 601.145 |
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no |
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Admin @ si @ MGH2013b |
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2351 |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Evaluation of Similarity Functions in Multimodal Stereo |
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Conference Article |
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Year |
2012 |
Publication |
9th International Conference on Image Analysis and Recognition |
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7324 |
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I |
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320-329 |
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Aveiro, Portugal |
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Abstract |
This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-31294-6 |
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ICIAR |
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ADAS |
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BLS2012a |
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2014 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Evaluating Color Representation for Online Road Detection |
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Conference Article |
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2013 |
Publication |
ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars |
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594-595 |
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Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. However, up to date, no comparison between these representations have been conducted. Therefore, in this paper, we perform an evaluation of existing color representations for road detection. More specifically, we focus on color planes derived from RGB data and their most com-
mon combinations. The evaluation is done on a set of 7000 road images acquired
using an on-board camera in different real-driving situations. |
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CVVT:E2M |
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ADAS;ISE |
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no |
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Admin @ si @ AGL2013 |
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2794 |
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Author |
Felipe Codevilla; Matthias Muller; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
End-to-end Driving via Conditional Imitation Learning |
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Conference Article |
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2018 |
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IEEE International Conference on Robotics and Automation |
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4693 - 4700 |
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Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at this https URL |
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Brisbane; Australia; May 2018 |
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ICRA |
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ADAS; 600.116; 600.124; 600.118 |
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Admin @ si @ CML2018 |
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3108 |
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Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure |
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Title |
Embedded Real-time Stixel Computation |
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Conference Article |
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2017 |
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GPU Technology Conference |
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GPU; CUDA; Stixels; Autonomous Driving |
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Silicon Valley; USA; May 2017 |
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GTC |
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ADAS; 600.118 |
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ADAS @ adas @ HEV2017a |
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2879 |
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Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
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Conference Article |
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2016 |
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16th International Conference on Computational Science |
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80 |
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143-153 |
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Autonomous Driving; Stereo; CUDA; 3d reconstruction |
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Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
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San Diego; CA; USA; June 2016 |
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ICCS |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HCE2016a |
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2740 |
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Author |
Joan Serrat; Enric Marti |
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Title |
Elastic matching using interpolation splines |
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1991 |
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IV Spanish Symposium of Pattern Recognition and image Analysis |
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ADAS;IAM; |
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IAM @ iam @ SMV1991 |
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1651 |
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Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Efficient On-Board Stereo Vision Pose Estimation |
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2007 |
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Computer Aided Systems Theory, Selected paper from |
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4739 |
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1183–1190 |
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This paper presents an efficient technique for real time estimation of on-board stereo vision system pose. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the 3D road points. Fast RANSAC fitting is obtained by selecting points according to a probability distribution function that takes into account the density of points at a given depth. Finally, stereo camera position
and orientation—pose—is computed relative to the road plane. The proposed technique is intended to be used on driver assistance systems for applications such as obstacle or pedestrian detection. A real time performance is reached. Experimental results on several environments and comparisons with a previous work are presented. |
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Las Palmas de Gran Canaria (Spain) |
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EUROCAST |
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ADAS |
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ADAS @ adas @ SDG2007b |
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916 |
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