SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.Comment: Accepted at AAAI'2

MobilitApp: Analysing mobility data of citizens in the metropolitan area of Barcelona

MobilitApp is a platform designed to provide smart mobility services in urban areas. It is designed to help citizens and transport authorities alike. Citizens will be able to access the MobilitApp mobile application and decide their optimal transportation strategy by visualising their usual routes, their carbon footprint, receiving tips, analytics and general mobility information, such as traffic and incident alerts. Transport authorities and service providers will be able to access information about the mobility pattern of citizens to o er their best services, improve costs and planning. The MobilitApp client runs on Android devices and records synchronously, while running in the background, periodic location updates from its users. The information obtained is processed and analysed to understand the mobility patterns of our users in the city of Barcelona, Spain

Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

In urban environments there are daily issues of traffic congestion which city authorities need to address. Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques. The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos. The relevant mobility metrics are calculated to conduct traffic analysis and measure the consequences of traffic congestion. The proposed approach is validated with the manual analysis results and the visualization results. The traffic analysis process is real-time in terms of the pre-trained model used

Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency

In this paper, we introduce a novel unsupervised domain adaptation technique for the task of 3D keypoint prediction from a single depth scan or image. Our key idea is to utilize the fact that predictions from different views of the same or similar objects should be consistent with each other. Such view consistency can provide effective regularization for keypoint prediction on unlabeled instances. In addition, we introduce a geometric alignment term to regularize predictions in the target domain. The resulting loss function can be effectively optimized via alternating minimization. We demonstrate the effectiveness of our approach on real datasets and present experimental results showing that our approach is superior to state-of-the-art general-purpose domain adaptation techniques.Comment: ECCV 201

Functional and formal component design for an electric motorbike “Sound Module”

Nowadays, new technologies allow creating new advances in the society through the innovation or improvement of existent products. This project intends to design a sound module that will be incorporated in an electric motorbike. As every motorbike has a different inside structure, the study will be carried out considering that the module’s volume may be adapted depending on the motorbike. The electric motorbike market is still in its development stage, and the studied topic in the project seems to be currently in research by many automotive enterprises, as the noise limit rulemakings in the city are a burning issue that has been already accomplished by 4-wheel vehicles. The design and study aims to contribute in the decrease of accidents due to the lack of noise of this type of vehicles. This will be accomplished with a selection of elements that combined, will allow the citizens to discern the presence of the electric vehicle and act in consequence