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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 |
Ferran Diego; G.D. Evangelidis; Joan Serrat |
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Title |
Night-time outdoor surveillance by mobile cameras |
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Conference Article |
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2012 |
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1st International Conference on Pattern Recognition Applications and Methods |
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2 |
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365-371 |
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This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods. |
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Algarve, Portugal |
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ICPRAM |
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ADAS |
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no |
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Admin @ si @ DES2012 |
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2035 |
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Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
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Title |
Fast Approximated Discriminative Common Vectors using rank-one SVD updates |
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Conference Article |
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2013 |
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20th International Conference On Neural Information Processing |
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8228 |
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III |
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368-375 |
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An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz |
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Daegu; Korea; November 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-42050-4 |
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ICONIP |
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ADAS |
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no |
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Admin @ si @ DFD2013 |
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2439 |
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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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no |
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Call Number |
Admin @ si @ DRC2017 |
Serial |
2988 |
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Author |
G.D. Evangelidis; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Slice Matching for Accurate Spatio-Temporal Alignment |
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Conference Article |
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2011 |
Publication |
In ICCV Workshop on Visual Surveillance |
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Keywords |
video alignment |
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Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works. |
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VS |
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ADAS |
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Admin @ si @ EDS2011; ADAS @ adas @ eds2011a |
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1861 |
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Author |
Zhijie Fang; Antonio Lopez |
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Title |
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
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Conference Article |
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2018 |
Publication |
IEEE Intelligent Vehicles Symposium |
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1271 - 1276 |
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Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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no |
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Admin @ si @ FaL2018 |
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3181 |
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Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis |
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Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification |
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2017 |
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3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition |
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Thessaloniki; Greece; October 2017 |
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IMEKOFOODS |
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ADAS; 600.118 |
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no |
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Admin @ si @ GDM2017 |
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3081 |
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Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Using the Average Landmark Vector Method for Robot Homing |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
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Title |
Monocular Depth Estimation by Learning from Heterogeneous Datasets |
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Conference Article |
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2018 |
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IEEE Intelligent Vehicles Symposium |
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2176 - 2181 |
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Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation. |
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IV |
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ADAS; 600.124; 600.116; 600.118 |
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Admin @ si @ GUH2018 |
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3183 |
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Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen |
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Quality Assessment of Optical Flow in Tagging MRI |
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2015 |
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5th Dutch Bio-Medical Engineering Conference BME2015 |
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The Netherlands; January 2015 |
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BME |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ KMF2015 |
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2616 |
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