A Highway-Driving System Design Viewpoint using an Agent-based Modeling of an Affordance-based Finite State Automata

This paper presents an agent-based modeling framework for affordance-based driving behaviors during the exit maneuver of driver agents in human-integrated transportation problems. We start our discussion from one novel modeling framework based on the concept of affordance called the Affordance-based Finite State Automata (AFSA) model, which incorporates the human perception of resource availability and action capability. Then, the agent-based simulation illustrates the validity of the AFSA framework for the Highway-Lane-Driver System. Next, the comparative study between real driving data and agent-based simulation outputs is provided using the transition diagram. Finally, we perform a statistical analysis and a correlation study to analyze affordance-based driving behavior of driver agents. The simulation results show that the AFSA model well represents the perception-based human actions and drivers??? characteristics, which are essential for the design viewpoint of control framework of human driver modeling. This study is also expected to benefit a designed control for autonomous/self-driving car in the future

Perception-based analytical technique of evacuation behavior under radiological emergency: An illustration of the Kori area

A simulation-based approach is proposed to study the protective actions taken by residents during nuclear emergencies using cognitive findings. Human perception-based behaviors are not heavily incorporated in the evacuation study for nuclear emergencies despite their known importance. This study proposes a generic framework of perception-based behavior simulation, in accordance with the ecological concept of affordance theory and a formal representation of affordance-based finite state automata. Based on the generic framework, a simulation model is developed to allow an evacuee to perceive available actions and execute one of them according to Newton & rsquo;s laws of motion. The case of a shadow evacuation under nuclear emergency is utilized to demonstrate the applicability of the proposed framework. The illustrated planning algorithm enables residents to compute not only prior knowledge of the environmental map, but also the perception of dynamic surroundings, using widely observed heuristics. The simulation results show that the temporal and spatial dynamics of the evacuation behaviors can be analyzed based on individual perception of circumstances, while utilizing the findings in cognitive science under unavoidable data restriction of nuclear emergencies. The perception-based analysis of the proposed framework is expected to enhance nuclear safety technology by complementing macroscopic analyses for advanced protective measures. (c) 2020 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

What is Robotics: Why Do We Need It and How Can We Get It?

Robotics is an emerging synthetic science concerned with programming work. Robot technologies are quickly advancing beyond the insights of the existing science. More secure intellectual foundations will be required to achieve better, more reliable and safer capabilities as their penetration into society deepens. Presently missing foundations include the identification of fundamental physical limits, the development of new dynamical systems theory and the invention of physically grounded programming languages. The new discipline needs a departmental home in the universities which it can justify both intellectually and by its capacity to attract new diverse populations inspired by the age old human fascination with robots. For more information: Kod*la

Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments

This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight. The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two differentComment: Ph.D Dissertatio

Risk-aware Path and Motion Planning for a Tethered Aerial Visual Assistant in Unstructured or Confined Environments

This research aims at developing path and motion planning algorithms for a tethered Unmanned Aerial Vehicle (UAV) to visually assist a teleoperated primary robot in unstructured or confined environments. The emerging state of the practice for nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments is to use two robots, a primary and a secondary that acts as a visual assistant to overcome the perceptual limitations of the sensors by providing an external viewpoint. However, the benefits of using an assistant have been limited for at least three reasons: (1) users tend to choose suboptimal viewpoints, (2) only ground robot assistants are considered, ignoring the rapid evolution of small unmanned aerial systems for indoor flying, (3) introducing a whole crew for the second teleoperated robot is not cost effective, may introduce further teamwork demands, and therefore could lead to miscommunication. This dissertation proposes to use an autonomous tethered aerial visual assistant to replace the secondary robot and its operating crew. Along with a pre-established theory of viewpoint quality based on affordances, this dissertation aims at defining and representing robot motion risk in unstructured or confined environments. Based on those theories, a novel high level path planning algorithm is developed to enable risk-aware planning, which balances the tradeoff between viewpoint quality and motion risk in order to provide safe and trustworthy visual assistance flight. The planned flight trajectory is then realized on a tethered UAV platform. The perception and actuation are tailored to fit the tethered agent in the form of a low level motion suite, including a novel tether-based localization model with negligible computational overhead, motion primitives for the tethered airframe based on position and velocity control, and two different approaches to negotiate tether with complex obstacle-occupied environments. The proposed research provides a formal reasoning of motion risk in unstructured or confined spaces, contributes to the field of risk-aware planning with a versatile planner, and opens up a new regime of indoor UAV navigation: tethered indoor flight to ensure battery duration and failsafe in case of vehicle malfunction. It is expected to increase teleoperation productivity and reduce costly errors in scenarios such as safe decommissioning and nuclear operations in the Fukushima Daiichi facility

Acquisition et exploitation des connaissances antérieures pour prédire le comportement des piétons autour des véhicules autonomes en environnement urbain

Autonomous Vehicles navigating in urban areas interact with pedestrians and other shared space users like bicycles throughout their journey either in open areas, like urban city centers, or closed areas, like parking lots. As more and more autonomous vehicles take to the city streets, their ability to understand and predict pedestrian behaviour becomes paramount. This is achieved by learning through continuous observation of the area to drive in. On the other hand, human drivers can instinctively infer pedestrian motion on an urban street even in previously unseen areas. This need for increasing a vehicle's situational awareness to reach parity with human drivers fuels the need for larger and deeper data on pedestrian motion in myriad situations and varying environments.This thesis focuses on the problem of reducing this dependency on large amounts of data to predict pedestrian motion accurately over an extended horizon. Instead, this work relies on Prior Knowledge, itself derived from the JJ Gibson's sociological principles of ``Natural Vision'' and ``Natural Movement''. It assumes that pedestrian behaviour is a function of the built environment and that all motion is directed towards reaching a goal. Knowing this underlying principle, the cost for traversing a scene from a pedestrian's perspective can be divined. Knowing this, inference on their behaviour can be performed. This work presents a contribution to the framework of understanding pedestrian behaviour as a confluence of probabilistic graphical models and sociological principles in three ways: modelling the environment, learning and predicting.Concerning modelling, the work assumes that there are some parts of the observed scene which are more attractive to pedestrians and some areas, repulsive. By quantifying these ``affordances'' as a consequence of certain Points of Interest (POIs) and the different elements in the scene, it is possible to model this scene under observation with different costs as a basis of the features contained within.Concerning learning, this work primarily extends the Growing Hidden Markov Model (GHMM) method - a variant of the Hidden Markov Model (HMM) probabilistic model- with the application of Prior Knowledge to initialise a topology able to infer accurately on ``typical motions'' in the scene. Secondly, the model that is generated behaves as a Self-Organising map, incrementally learning non-typical pedestrian behaviour and encoding this within the topology while updating the parameters of the underlying HMM.On prediction, this work carries out Bayesian inference on the generated model and can, as a result of Prior Knowledge, manage to perform better than the existing implementation of the GHMM method in predicting future pedestrian positions without the availability of training trajectories, thereby allowing for its utilisation in an urban scene with only environmental data.The contributions of this thesis are validated through experimental results on real data captured from an overhead camera overlooking a busy urban street, depicting a structured built environment and from the car's perspective in a parking lot, depicting a semi-structured environment and tested on typical and non-typical trajectories in each case.Les véhicules autonomes qui naviguent dans les zones urbaines interagissent avec les piétons et les autres utilisateurs de l'espace partagé, comme les bicyclettes, tout au long de leur trajet, soit dans des zones ouvertes, comme les centres urbains, soit dans des zones fermées, comme les parcs de stationnement. Alors que de plus en plus de véhicules autonomes sillonnent les rues de la ville, leur capacité à comprendre et à prévoir le comportement des piétons devient primordiale. Ceci est possible grâce à l'apprentissage par l'observation continue de la zone à conduire. D'autre part, les conducteurs humains peuvent instinctivement déduire le mouvement des piétons sur une rue urbaine, même dans des zones auparavant invisibles. Ce besoin d'accroître la conscience de la situation d'un véhicule pour atteindre la parité avec les conducteurs humains alimente le besoin de données plus vastes et plus approfondies sur le mouvement des piétons dans une myriade de situations et d'environnements variés.Cette thèse porte sur le problème de la réduction de cette dépendance à l'égard de grandes quantités de données pour prédire avec précision les mouvements des piétons sur un horizon prolongé. Ce travail s'appuie plutôt sur la connaissance préalable, elle-même dérivée des principes sociologiques de "Vision naturelle" et de "Mouvement naturel" du JJ Gibson. Il suppose que le comportement des piétons est fonction de l'environnement bâti et que tous les mouvements sont orientés vers l'atteinte d'un but. Connaissant ce principe sous-jacent, le coût de la traversée d'une scène du point de vue d'un piéton peut être deviné. Sachant cela, on peut en déduire leur comportement. Cet ouvrage apporte une contribution au cadre de compréhension du comportement piétonnier en tant que confluent de modèles graphiques probabilistes et de principes sociologiques de trois façons : modélisation de l'environnement, apprentissage et prévision.En ce qui concerne la modélisation, le travail suppose que certaines parties de la scène observée sont plus attrayantes pour les piétons et que d'autres sont répugnantes. En quantifiant ces " affordances " en fonction de certains Points d'Intérêt (POI) et des différents éléments de la scène, il est possible de modéliser cette scène sous observation avec différents coûts comme base des caractéristiques qu'elle contient.En ce qui concerne l'apprentissage, ce travail étend principalement la méthode du Modèle de Markov Caché Croissant (GHMM) - une variante du modèle probabiliste du Modèle de Markov Caché (HMM) - avec l'application des connaissances préalables pour initialiser une topologie capable de déduire avec précision les " mouvements types " dans la scène. Deuxièmement, le modèle généré se comporte comme une carte auto-organisatrice, apprenant progressivement un comportement piétonnier atypique et le codant dans la topologie tout en mettant à jour les paramètres du HMM sous-jacent.Sur la prédiction, ce travail effectue une inférence bayésienne sur le modèle généré et peut, grâce aux connaissances préalables, réussir à mieux prédire les positions futures des piétons sans disposer de trajectoires de formation, ce qui permet de l'utiliser dans un environnement urbain avec uniquement des données environnementales, que la méthode GHMM actuellement en application.Les contributions de cette thèse sont validées par des résultats expérimentaux sur des données réelles capturées à partir d'une caméra aérienne surplombant une rue urbaine très fréquentée, représentant un environnement bâti structuré et du point de vue de la voiture dans un parking, représentant un environnement semi-structuré et testé sur des trajectoires typiques et atypiques dans chaque cas