Seguimento ativo de agentes dinâmicos multivariados usando informação vectorial

Doutoramento em Engenharia MecânicaO objeto principal da presente tese é o estudo de sistemas avançados de segurança, no âmbito da segurança automóvel, baseando-se na previsão de movimentos e ações dos agentes externos. Esta tese propõe tratar os agentes como entidades dinâmicas, com motivações e constrangimentos próprios. Apresenta-se, para tal, novas técnicas de seguimento dos referidos agentes levando em linha de conta as suas especificidades. Em decorrência, estuda-se dedicadamente dois tipos de agentes: os veículos automóveis e os peões. Quanto aos veículos automóveis, propõe-se melhorar a capacidade de previsão de movimentos recorrendo a modelos avançados que representam corretamente os constrangimentos presentes nos veículos. Assim, foram desenvolvidos algoritmos avançados de seguimento de agentes com recurso a modelos de movimento não holonómicos. Estes algoritmos fazem uso de dados vectoriais de distância fornecidos por sensores de distância laser. Para os peões, devido à sua complexidade (designadamente a ausência de constrangimentos de movimentos) propõe-se que a análise da sua linguagem corporal permita detetar atempadamente possíveis intenções de movimentos. Assim, foram desenvolvidos algoritmos de perceção de pose de peões adaptados ao campo da segurança automóvel com recurso a uso de dados de distâncias 3D obtidos com uma câmara stereo. De notar que os diversos algoritmos foram testados em experiências realizadas em ambiente real.The main topic of this thesis is the study of advanced safety systems, in the field of automotive safety, based on the prediction of the movement and actions of external agents. This thesis proposes to treat the agents as dynamic entities with their own motivations as constraints. As so, new target tracking techniques are proposed taking into account the targets’ specificities. Therefore, two different types of agents are dedicatedly studied: automobile vehicles and pedestrians. For the automobile vehicles, a technique to improve motion prediction by the use of advanced motion models is proposed, these models will correctly represent the constrains that exist in this kind of vehicle. With this goal, advanced target tracking algorithms coupled with nonholonomic motion models were developed. These algorithms make use of vectorial range data supplied by laser range sensors. Concerning the pedestrians, due to the problem complexity (mainly due to the lack of any specific motion constraint), it is proposed that the analysis of the pedestrians body language will allow to detected early the pedestrian intentions and movements. As so, pedestrian pose estimation algorithms specially adapted to the field of automotive safety were developed; these algorithms use 3D point cloud data obtained with a stereo camera. The various algorithms were tested in experiments conducted in real conditions

Monocular depth cues in computer vision applications

In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. In our thesis, we have demonstrated how coarse depth information can be integrated in different tasks following alternative strategies to obtain more precise and robust results in three computer vision applications: camera rotation parameters estimation, background estimation and pedestrian candidate generation

Environment perception based on LIDAR sensors for real road applications

The recent developments in applications that have been designed to increase road safety require reliable and trustworthy sensors. Keeping this in mind, the most up-to-date research in the field of automotive technologies has shown that LIDARs are a very reliable sensor family. In this paper, a new approach to road obstacle classification is proposed and tested. Two different LIDAR sensors are compared by focusing on their main characteristics with respect to road applications. The viability of these sensors in real applications has been tested, where the results of this analysis are presented.The recent developments in applications that have been designed to increase road safety require reliable and trustworthy sensors. Keeping this in mind, the most up-to-date research in the field of automotive technologies has shown that LIDARs are a very reliable sensor family. In this paper, a new approach to road obstacle classification is proposed and tested. Two different LIDAR sensors are compared by focusing on their main characteristics with respect to road applications. The viability of these sensors in real applications has been tested, where the results of this analysis are presented.The work reported in this paper has been partly funded by the Spanish Ministry of Science and Innovation (TRA2007- 67786-C02-01, TRA2007-67786-C02-02, and TRA2009- 07505) and the CAM project SEGVAUTO-II.Publicad

Using Road Topology to Improve Cyclist Path Prediction

We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then spatially aligned to local curves and crossings in the road. We study a standard approach for path prediction in the literature based on Kalman Filters, as well as a mixture of specialized filters related to specific road orientations at junctions. Our experiments demonstrate an improved prediction accuracy (up to 20% on sharp turns) of mixing specialized motion models for canonical directions, and prior knowledge on the road topology. The new track data complements the existing video, disparity and annotation data o

Fusion Based Safety Application for Pedestrian Detection with Danger Estimation

Proceedings of: 14th International Conference on Information Fusion (FUSION 2011). Chicago, Illinois, USA 5-8 July 2011.Road safety applications require the most reliable data. In recent years data fusion is becoming one of the main technologies for Advance Driver Assistant Systems (ADAS) to overcome the limitations of isolated use of the available sensors and to fulfil demanding safety requirements. In this paper a real application of data fusion for road safety for pedestrian detection is presented. Two sets of automobile-emplaced sensors are used to detect pedestrians in urban environments, a laser scanner and a stereovision system. Both systems are mounted in the automobile research platform IVVI 2.0 to test the algorithms in real situations. The different safety issues necessary to develop this fusion application are described. Context information such as velocity and GPS information is also used to provide danger estimation for the detected pedestrians.This work was supported by the Spanish Government through the Cicyt projects FEDORA (GRANT TRA2010- 20225-C03-01 ) , VIDAS-Driver (GRANT TRA2010-21371-C03-02 ).Publicad

Image-Based Lateral Position, Steering Behavior Estimation, and Road Curvature Prediction for Motorcycles

International audienceThis letter presents an image-based approach to simultaneously estimate the lateral position of a powered-two-wheeled vehicle on the road, its steering behavior and predict the road curvature ahead of the motorcycle. This letter is based on the inverse perspective mapping technique combined with a road lanes detection algorithm capable of detecting straight and curved lanes. Then, a clothoid model is used to extract pertinent information from the detected road markers. Finally, the performance of the proposed approach is illustrated through simulations carried out with the well-known motorcycle simulator “BikeSim.” The results are very promising since the algorithm is capable of estimating, in real time, the road geometry and the vehicle location with a better accuracy than the one given by the commercial GPS

A Review of the Bayesian Occupancy Filter

Autonomous vehicle systems are currently the object of intense research within scientific and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into five progressive layers, from the level closest to the sensor to the highest abstract level of risk assessment. In addition, we present a study of implemented use cases to provide a practical understanding on the main uses of the BOF and its taxonomy.This work has been founded by the Spanish Ministry of Economy and Competitiveness along with the European Structural and Investment Funds in the National Project TCAP-AUTO (RTC-2015-3942-4) in the program of “Retos Colaboración 2014”

What can be done with an embedded stereo-rig in urban environments?

International audienceThe development of the Autonomous Guided Vehicles (AGVs) with urban applications are now possible due to the recent solutions (DARPA Grand Challenge) developed to solve the Simultaneous Localization And Mapping (SLAM) problem: perception, path planning and control. For the last decade, the introduction of GPS systems and vision have been allowed the transposition of SLAM methods dedicated to indoor environments to outdoor ones. When the GPS data are unavailable, the current position of the mobile robot can be estimated by the fusion of data from odometer and/or Inertial Navigation System (INS). We detail in this article what can be done with an uncalibrated stereo-rig, when it is embedded in a vehicle which is going through urban roads. The methodology is based on features extracted on planes: we mainly assume the road at the foreground as the plane common to all the urban scenes but other planes like vertical frontages of buildings can be used if the features extracted on the road are not enough relevant. The relative motions of the coplanar features tracked with both cameras allow us to stimate the vehicle ego-motion with a high precision. Futhermore, the features which don't check the relative motion of the considered plane can be assumed as obstacles

Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion

Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale. While disambiguating scale during training is not possible without some kind of ground truth supervision, having scale consistent depth predictions would make it possible to calculate scale once during inference as a post-processing step and use it over-time. With this as a goal, a set of temporal consistency losses that minimize pose inconsistencies over time are introduced. Evaluations show that introducing these constraints not only reduces depth inconsistencies but also improves the baseline performance of depth and ego-motion prediction.Comment: Scandinavian Conference on Image Analysis (SCIA) 202