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

A multiframe assignment algorithm for single sensor bearings-only tracking

Bearings-only tracking (BOT) using a single maneuvering platform has been studied extensively in the past. However, only a few studies exist in the open literature that deal with measurement origin uncertainty. Most publications are concerned with finding the best filtering approach, since BOT is inherently nonlinear, or finding the optimal maneuver strategy for the sensor platform to improve observability. We consider measurement origin uncertainty due to the existence of multiple targets in the surveillance region, non-unity detection probability, and false alarms. Our algorithm uses the multiframe assignment (MFA) to solve the data association problem, and filtering is performed using the unscented Kalman filter (UKF). We employ both the modified and log polar coordinate systems. Simulation results show that the proposed algorithm is very effective in terms of tracking accuracy and track maintenance capability, especially when formulated in the log polar coordinate system.T. Sathyan, A. Sinha, M. Mallic