Is the Pedestrian going to Cross? Answering by 2D Pose Estimation

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-of-the-art results.Comment: This is a paper presented in IEEE Intelligent Vehicles Symposium (IEEE IV 2018

Resource location based on precomputed partial random walks in dynamic networks

The problem of finding a resource residing in a network node (the \emph{resource location problem}) is a challenge in complex networks due to aspects as network size, unknown network topology, and network dynamics. The problem is especially difficult if no requirements on the resource placement strategy or the network structure are to be imposed, assuming of course that keeping centralized resource information is not feasible or appropriate. Under these conditions, random algorithms are useful to search the network. A possible strategy for static networks, proposed in previous work, uses short random walks precomputed at each network node as partial walks to construct longer random walks with associated resource information. In this work, we adapt the previous mechanisms to dynamic networks, where resource instances may appear in, and disappear from, network nodes, and the nodes themselves may leave and join the network, resembling realistic scenarios. We analyze the resulting resource location mechanisms, providing expressions that accurately predict average search lengths, which are validated using simulation experiments. Reduction of average search lengths compared to simple random walk searches are found to be very large, even in the face of high network volatility. We also study the cost of the mechanisms, focusing on the overhead implied by the periodic recomputation of partial walks to refresh the information on resources, concluding that the proposed mechanisms behave efficiently and robustly in dynamic networks.Comment: 39 pages, 25 figure

On Offline Evaluation of Vision-based Driving Models

Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. The supplementary video can be viewed at https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc

Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving

Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just $1$ bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet- and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.Comment: Accepted by ICRA 201

Exploring the Limitations of Behavior Cloning for Autonomous Driving

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, including in unseen environments, executing complex lateral and longitudinal maneuvers without these reactions being explicitly programmed. However, we confirm well-known limitations (due to dataset bias and overfitting), new generalization issues (due to dynamic objects and the lack of a causal model), and training instability requiring further research before behavior cloning can graduate to real-world driving. The code of the studied behavior cloning approaches can be found at https://github.com/felipecode/coiltraine

Modelo de cuantificación del consumo energético en edificación

The research conducted in this paper focuses on the generation of a model for the quantification of energy consumption in building. This is to be done through one of the most relevant environmental impact indicators associated with weight per m2 of construction, as well as the energy consumption resulting from the manufacturing process of materials used in building construction. The practical application of the proposed model on different buildings typologies in Seville, will provide information regarding the building materials, the subsystems and the most relevant construction elements. Hence, we will be able to observe the impact the built surface has on the environment. The results obtained aim to reference the scientific community, providing quantitative data comparable to other types of buildings and geographical areas. Furthermore, it may also allow the analysis and the characterization of feasible solutions to reduce the environmental impact generated by the different materials, subsystems and construction elements commonly used in the different building types defined in this study.La investigación realizada en el presente trabajo plantea la generación de un modelo de cuantificación del consumo energético en edificación, a través de uno de los indicadores de impacto ambiental más relevantes asociados al peso por m2 de construcción, el consumo energético derivado del proceso de fabricación de los materiales de construcción empleados en edificación. La aplicación práctica del modelo propuesto sobre diferentes tipologías edificatorias en Sevilla aportará información respecto a los materiales de construcción, subsistemas y elementos constructivos más impactantes, permitiendo visualizar la influencia que presenta la superficie construida en cuanto al impacto ambiental generado. Los resultados obtenidos pretenden servir de referencia a la comunidad científica, aportando datos numéricos que podrán ser comparados en otras tipologías y ámbitos geográficos, a la vez que permitirán analizar y precisar mejoras en cuanto al impacto ambiental generado por los diferentes materiales, subsistemas y elementos constructivos habitualmente utilizados en las tipologías edificatorias definidas

ARCE CARRASCOSO, J.L. Teoría del Conocimiento. Sujeto, Lenguaje, Mundo.

Sin resume

Spatiotemporal Stacked Sequential Learning for Pedestrian Detection

Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.Comment: 8 pages, 5 figure, 1 tabl

Yolanda Ruano de La Fuente (1962-2008). In Memoriam

Sin resume

Foundation epistemology of human sciences (The dialogue of Habermas to Dilthey)

The thesis that I wish to discuss in this paper is the next: Human Sciences (Cultural Sciences according to Dilthey, Historic-Hermeneutic sciences according to Habermas) are possible as far as the method that should make all of them possible can be epistemologically justified, this method is the hermeneutic understanding of meaning, whose central point is, in fact, the hermeneutic circle. The matter is to try to justify epistemologically the hermeneutic as understanding the meaning of the own and the other ones life experiences within reflexivity and reciprocity that impregnates the structure of ordinary language. In the dialogue Habermas to Dilthey, in Knowledge and Human Interests appear many arguments to this foundation. Our text will make special emphasis in the next items: distinction between natural sciences and cultural sciences, hermeneutic understanding of meaning, ordinary language and reflexivity, specificity of the hermeneutic circle, science an vital context, and, finally, we are going to make an account of the Habermas interpretation of the Dilthey start point in bases to the relationships between Historic-hermeneutic sciences and the practice interest of the knowledge.La tesis que deseo discutir en este trabajo es la siguiente: las ciencias humanas (ciencias del espíritu según Dilthey, ciencias histórico-hermenéuticas según Habermas) son posibles en la medida en que pueda ser epistemológicamente justificado el método que las hace posibles, a saber, la comprensión hermenéutica del sentido cuyo núcleo central lo constituye el círculo hermenéutico. Se trata, pues, de justificar epistemológicamente la hermenéutica como comprensión del sentido de las vivencias propias y ajenas, para lo cual es preciso analizar la reflexividad y reciprocidad que impregna la estructura del lenguaje ordinario. En el diálogo que Habermas establece con Dilthey en Conocimiento e interés aparecen los suficientes argumentos para dicha fundamentación. Nuestro texto hará especial hincapié en los siguientes tópicos: distinción entre ciencias de la naturaleza y ciencias del espíritu, comprensión hermenéutica del sentido, lenguaje ordinario y reflexividad, especificidad del círculo hermenéutico, ciencia y contexto vital, y, finalmente, haremos un balance de la interpretación habermasiana de Dilthey a partir de las relaciones entre ciencias histórico-hermenéuticas e interés práctico del conocimiento