2D laser-based probabilistic motion tracking in urban-like environments

All over the world traffic injuries and fatality rates are increasing every year. The combination of negligent and imprudent drivers, adverse road and weather conditions produces tragic results with dramatic loss of life. In this scenario, the use of mobile robotics technology onboard vehicles could reduce casualties. Obstacle motion tracking is an essential ability for car-like mobile robots. However, this task is not trivial in urban environments where a great quantity and variety of obstacles may induce the vehicle to take erroneous decisions. Unfortunately, obstacles close to its sensors frequently cause blind zones behind them where other obstacles could be hidden. In this situation, the robot may lose vital information about these obstructed obstacles that can provoke collisions. In order to overcome this problem, an obstacle motion tracking module based only on 2D laser scan data was developed. Its main parts consist of obstacle detection, obstacle classification, and obstacle tracking algorithms. A motion detection module using scan matching was developed aiming to improve the data quality for navigation purposes; a probabilistic grid representation of the environment was also implemented. The research was initially conducted using a MatLab simulator that reproduces a simple 2D urban-like environment. Then the algorithms were validated using data samplings in real urban environments. On average, the results proved the usefulness of considering obstacle paths and velocities while navigating at reasonable computational costs. This, undoubtedly, will allow future controllers to obtain a better performance in highly dynamic environments.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES

Path Planning, Replanning, and Execution for Autonomous Driving in Urban and Offroad Environments

Abstract — We present an autonomous driving system that is capable of planning, replanning, and executing paths for driving in urban and offroad environments. For planning, we rely on the E ∗ algorithm which computes a smooth navigation function that takes into account traversibility information extracted from laser scans. The path execution algorithm is centered around a kinodynamic controller which follows the gradient of the navigation function. This work is based on prior experience with the SmartTer vehicle, which we are in the process of updating, and the focus is on integration. I

Image Understanding and Data Fusion for Driver Assistant Systems

Sensoren und die Verarbeitung von Sensordaten stellen einen Eckpfeiler der erfolgreichen Implementierung von Fahrerassistenzsystemen dar, die entscheidend zu erhöhter Verkehrssicherheit beitragen können. In diesem Artikel stellen wir einen Ansatz vor, der Bildverarbeitung in hochdynamischen Verkehrssituationen zur Erkennung von Fahrspur und anderen Fahrzeugen mit Sensoren verbindet, die schon heute breiten Einsatz in Serienfahrzeugen bringen. Dabei gehen wir zum einen auf die Besonderheiten unserer Spur- und Fahrzeugerkennung ein und beschreiben zum anderen, wie probabilisitische Datenfusion in Echtzeit zu deutlich verbesserten Erkenntnissen über die Fahrsituation führen, und so besseren und sichereren Fahrerassistenzsystemen beitragen