Breast cancer cases are steadily increasing over the years. To improve the chances of a successful treatment, it is essential to diagnose and treat lesions in the initial stages. Screening methods, such as mammography, are one of the most effective strategies to achieve this goal. However, identifying some types of lesions can be challenging due to their morphological structure. For instance, calcifications typically have small diameters that range from 0.1mm to 1mm. Detecting calcifications require a careful and detailed analysis of the exams by radiologists. This work proposes a system for detecting malignant calcifications in mammograms using deep convolutional neural networks to assist in this task. The mammography image is split into patches that are classified by the deep convolutional neural network as containing malignant lesions (cancerous tissue) or not (healthy tissue). The patch-wise probabilities of cancer are then summarized so that a whole mammogram decision can be obtained. Moreover, a heatmap is built highlighting regions with a high probability of containing cancerous tissue. This heatmap can be used to draw the attention of radiologists to specific areas. Experiments performed with the CBIS-DDSM dataset indicate that the best model evaluated achieved the F-Measure of 0.99 in the classification task. These results indicate that applying the system in a real environment could improve the detection of cancer cases.

We propose a new approach for real time inference of occupancy maps for self-driving cars using deep neural networks (DNN) named NeuralMapper. NeuralMapper receives LiDAR sensor data as input and generates as output the occupancy grid map around the car. NeuralMapper infers the probability of each grid map cell from one of the three following classes: Occupied, Free and Unknown. The system was tested with two datasets and achieved an average accuracy of 76.48% and 73.81%. We also evaluated our approach for localization purposes in a self-driving car and most of the localization pose errors were less than 0.20m with an RMSE of 0.28 which are close to the results in the literature for methods using other grid mapping approaches.

We propose an image-based real-time path planner for the self-driving car IARA, named DeepPath. DeepPath uses a CNN for inferring paths from images. During the self-driving car operation, DeepPath receives an image and the current car pose. Then, it sends the image to a CNN trained to infer a model of the path. After that, DeepPath generates the path in the IARA's coordinate system using the path model. Subsequently, given the current IARA's pose, DeepPath transforms each pose of the path in the IARA's coordinate system into another pose in the world coordinate system. Finally, it sends the path to the IARA's Behavior Selector subsystem, the next subsystem in the IARA's Decision-Making system. We evaluated the performance of DeepPath in real world scenarios. Our results showed that DeepPath is able to correctly generate paths for IARA that differ only slightly from those defined by humans.

We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car. The DCNN receives as input (i) an image, which is captured by a camera installed on IARA's roof; and (ii) crops of the image, which are centered in the waypoints computed by IARA's path planner and whose sizes increase with the distance from IARA. We employ an overlap filter to discard detections of the same car in different crops of the same image based on the percentage of overlap of detections' bounding boxes. We evaluated the performance of the proposed augmented-range vehicle detection system (ARVDS) using the hardware and software infrastructure available in the IARA self-driving car. Using IARA, we captured thousands of images of real traffic situations containing cars in a long range. Experimental results show that ARVDS increases the Average Precision (AP) of long range car detection from 29.51% (using a single whole image) to 63.15%.

Autonomous terrestrial vehicles must be capable of perceiving traffic lights and recognizing their current states to share the streets with human drivers. Most of the time, human drivers can easily identify the relevant traffic lights. To deal with this issue, a common solution for autonomous cars is to integrate recognition with prior maps. However, additional solution is required for the detection and recognition of the traffic light. Deep learning techniques have showed great performance and power of generalization including traffic related problems. Motivated by the advances in deep learning, some recent works leveraged some state-of-the-art deep detectors to locate (and further recognize) traffic lights from 2D camera images. However, none of them combine the power of the deep learning-based detectors with prior maps to recognize the state of the relevant traffic lights. Based on that, this work proposes to integrate the power of deep learning-based detection with the prior maps used by our car platform IARA (acronym for Intelligent Autonomous Robotic Automobile) to recognize the relevant traffic lights of predefined routes. The process is divided in two phases: an offline phase for map construction and traffic lights annotation; and an online phase for traffic light recognition and identification of the relevant ones. The proposed system was evaluated on five test cases (routes) in the city of Vitória, each case being composed of a video sequence and a prior map with the relevant traffic lights for the route. Results showed that the proposed technique is able to correctly identify the relevant traffic light along the trajectory.

Advanced Driver Assistance Systems (ADAS) have experienced major advances in the past few years. The main objective of ADAS includes keeping the vehicle in the correct road direction, and avoiding collision with other vehicles or obstacles around. In this paper, we address the problem of estimating the heading direction that keeps the vehicle aligned with the road direction. This information can be used in precise localization, road and lane keeping, lane departure warning, and others. To enable this approach, a large-scale database 1+ million images) was automatically acquired and annotated using publicly available platforms such as the Google Street View API and OpenStreetMap. After the acquisition of the database, a CNN model was trained to predict how much the heading direction of a car should change in order to align it to the road 4 meters ahead. To assess the performance of the model, experiments were performed using images from two different sources: a hidden test set from Google Street View (GSV) images and two datasets from our autonomous car (IARA). The model achieved a low mean average error of 2.359° and 2.524° for the GSV and IARA datasets, respectively; performing consistently across the different datasets. It is worth noting that the images from the IARA dataset are very different (camera, FOV, brightness, etc.) from the ones of the GSV dataset, which shows the robustness of the model. In conclusion, the model was trained effortlessly (using automatic processes) and showed promising results in real-world databases working in real-time (more than 75 frames per second).

We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations. We take a segmentation approach to the problem and use the Efficient Neural Network (ENet) DNN for segmenting LiDAR remission grid maps into road maps. We represent road maps using what we called road grid maps. Road grid maps are square matrixes and each element of these matrixes represents a small square region of real-world space. The value of each element is a code associated with the semantics of the road map. Our road grid maps contain all information about the roads' lanes required for building the Road Definition Data Files (RDDFs) that are necessary for the operation of our autonomous car, IARA (Intelligent Autonomous Robotic Automobile). We have built a dataset of tens of kilometers of manually marked road lanes and used part of it to train ENet to segment road grid maps from remission grid maps. After being trained, ENet achieved an average segmentation accuracy of 83.7%. We have tested the use of inferred road grid maps in the real world using IARA on a stretch of 3.7 km of urban roads and it has shown performance equivalent to that of the previous IARA's subsystem that uses a manually generated RDDF.

In this work, we propose a subsystem to handle pedestrians in crosswalks using deep neural networks for the IARA autonomous car, which relies on camera and LIDAR data fusion. Crosswalks' positions were manually annotated in IARA's map. Pedestrians are detected in the camera image using a convolutional neural network (CNN). Then, pedestrians' positions in the map are obtained by fusing their positions in the image with the LIDAR point cloud. Subsequently, if a pedestrian position is inside the crosswalk area, the crosswalk is set as busy. Finally, a busy crosswalk message is published to the High-Level Decision Maker subsystem. This subsystem selects the car's behavior according to the crosswalk condition and propagates this decision down through the control pipeline, in order to make the car drive correctly through the crosswalk area. The Pedestrian Handler subsystem was evaluated on IARA, which was driven autonomously for various laps along a real and complex circuit with various crosswalks. In all passages through crosswalks, the Pedestrian Handler dealt with pedestrians as expected, i.e., without any human intervention.

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of occasional obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).

In this work, we present a software architecture to solve, at some level, the follow the leader problem. This problem consists of an autonomous vehicle trying to track and follow a leader vehicle. To track the leader position in consecutive camera images, we employed the Generic Object Tracking Using Regression Networks (GOTURN). GOTURN is a pre-trained Deep Neural Network capable of tracking generic objects, without application-specific training or fine-tuning. The proposed software architecture was evaluated using a real autonomous vehicle, in four stretches of a University ring road. In all experiments, the autonomous vehicle was able to follow the leader's path with maximum root mean square error of 0.28m.