Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking

Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control

Laser-Based Detection and Tracking of Moving Obstacles to Improve Perception of Unmanned Ground Vehicles

El objetivo de esta tesis es desarrollar un sistema que mejore la etapa de percepción de vehículos terrestres no tripulados (UGVs) heterogéneos, consiguiendo con ello una navegación robusta en términos de seguridad y ahorro energético en diferentes entornos reales, tanto interiores como exteriores. La percepción debe tratar con obstáculos estáticos y dinámicos empleando sensores heterogéneos, tales como, odometría, sensor de distancia láser (LIDAR), unidad de medida inercial (IMU) y sistema de posicionamiento global (GPS), para obtener la información del entorno con la precisión más alta, permitiendo mejorar las etapas de planificación y evitación de obstáculos. Para conseguir este objetivo, se propone una etapa de mapeado de obstáculos dinámicos (DOMap) que contiene la información de los obstáculos estáticos y dinámicos. La propuesta se basa en una extensión del filtro de ocupación bayesiana (BOF) incluyendo velocidades no discretizadas. La detección de velocidades se obtiene con Flujo Óptico sobre una rejilla de medidas LIDAR discretizadas. Además, se gestionan las oclusiones entre obstáculos y se añade una etapa de seguimiento multi-hipótesis, mejorando la robustez de la propuesta (iDOMap). La propuesta ha sido probada en entornos simulados y reales con diferentes plataformas robóticas, incluyendo plataformas comerciales y la plataforma (PROPINA) desarrollada en esta tesis para mejorar la colaboración entre equipos de humanos y robots dentro del proyecto ABSYNTHE. Finalmente, se han propuesto métodos para calibrar la posición del LIDAR y mejorar la odometría con una IMU

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Detection-assisted Object Tracking by Mobile Cameras

Tracking-by-detection is a class of new tracking approaches that utilizes recent development of object detection algorithms. This type of approach performs object detection for each frame and uses data association algorithms to associate new observations to existing targets. Inspired by the core idea of the tracking-by-detection framework, we propose a new framework called detection-assisted tracking where object detection algorithm provides help to the tracking algorithm when such help is necessary; thus object detection, a very time consuming task, is performed only when needed. The proposed framework is also able to handle complicated scenarios where cameras are allowed to move, and occlusion or multiple similar objects exist. We also port the core component of the proposed framework, the detector, onto embedded smart cameras. Contrary to traditional scenarios where the smart cameras are assumed to be static, we allow the smart cameras to move around in the scene. Our approach employs histogram of oriented gradients (HOG) object detector for foreground detection, to enable more robust detection on mobile platform. Traditional background subtraction methods are not suitable for mobile platforms where the background changes constantly. Adviser: Senem Velipasalar and Mustafa Cenk Gurso

Dataset of Panoramic Images for People Tracking in Service Robotics

We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility.We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance