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Smart services ecosystem concept for cooperative ITS 22

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Smart services ecosystem concept for cooperative ITS

11th Euro-American Conference on Telematics and Information Systems, EATIS 2022

作者： Kozhevnikov, Sergey Svitek, Miroslav Chou, Shuo-Yan Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Faculty of Transportation Sciences Czech Technical University in Prague Czech Republic Department of Industrial Management National Taiwan University of Science and Technology Taiwan

ISBN: (纸本)9781450397391

Current intelligent transport systems (ITS) and trends in their development are weekly consider all involved elements and do not cover the topic of cooperative work of services. In this paper the concept of the cooperative ITS as a digital ecosystem (DE) of services, sensors, hardware devices and users is presented. This paper proposes a new approach based on the theory of complex adaptive systems and bio-inspired artificial intelligence (AI), which can show emergent intelligence behaviour. The proposed approach and software platform should not only integrate all smart transport services, but also provide control, self-organization and evolution of ITS. The software platform developed as DE enables physical or virtual organizations provide core services based on real time schedules and current state of their resources automatically, reactively or pro-actively, and only to the customers interested in these services. It will increase the service level for clients, reduce costs and delivery time, improve scalability, performance and reliability of ITS. © 2022 ACM.

关键词： Ecosystems

GROUNDED VIDEO CAPTION GENERATION

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arXiv 2024年

作者： Kazakos, Evangelos Schmid, Cordelia Sivic, Josef Czech Institute of Informatics Robotics and Cybernetics The Czech Technical University Prague Czech Republic Inria École normale supérieure CNRS PSL Research University France

We propose a new task, dataset and model for grounded video caption generation. This task unifies captioning and object grounding in video, where the objects in the caption are grounded in the video via temporally consistent bounding boxes. We introduce the following contributions. First, we present a task definition and a manually annotated test dataset for this task, referred to as GROunded Video Caption Generation (GROC). Second, we introduce a large-scale automatic annotation method leveraging an existing model for grounded still image captioning together with an LLM for summarising frame-level captions into temporally consistent captions in video. Furthermore, we prompt the LLM to track by language - classifying noun phrases from the frame-level captions into noun phrases of the video-level generated caption. We apply this approach to videos from the HowTo100M dataset, which results in a new large-scale training dataset, called HowToGround, with automatically annotated captions and spatio-temporally consistent bounding boxes with coherent natural language labels. Third, we introduce a new grounded video caption generation model, called VideoGround, and train the model on the new automatically annotated HowToGround dataset. Finally, results of our VideoGround model set the state of the art for the new task of grounded video caption generation. We perform extensive ablations and demonstrate the importance of key technical contributions of our model. © 2024, CC BY-NC-SA.

关键词： Electric grounding

Self-Supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement

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arXiv 2024年

作者： Pietrantoni, Maxime Csurka, Gabriela Humenberger, Martin Sattler, Torsten Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic NAVER LABS Europe France

Visual localization techniques rely upon some underlying scene representation to localize against. These representations can be explicit such as 3D SFM map or implicit, such as a neural network that learns to encode the scene. The former requires sparse feature extractors and matchers to build the scene representation. The latter might lack geometric grounding not capturing the 3D structure of the scene well enough. This paper proposes to jointly learn the scene representation along with a 3D dense feature field and a 2D feature extractor whose outputs are embedded in the same metric space. Through a contrastive framework we align this volumetric field with the image-based extractor and regularize the latter with a ranking loss from learned surface information. We learn the underlying geometry of the scene with an implicit field through volumetric rendering and design our feature field to leverage intermediate geometric information encoded in the implicit field. The resulting features are discriminative and robust to viewpoint change while maintaining rich encoded information. Visual localization is then achieved by aligning the image-based features and the rendered volumetric features. We show the effectiveness of our approach on real-world scenes, demonstrating that our approach outperforms prior and concurrent work on leveraging implicit scene representations for localization. © 2024, CC BY.

关键词： Rendering (computer graphics)

Collision Detection Accelerated: An Optimization Perspective 18th

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Collision Detection Accelerated: An Optimization Perspective

18th robotics: Science and Systems, RSS 2022

作者： Montaut, Louis Le Lidec, Quentin Petrik, Vladimir Sivic, Josef Carpentier, Justin Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic Inria and Département d’Informatique de l’École Normale Supérieure PSL Research University Paris France

ISBN: (纸本)9780992374785

Collision detection between two convex shapes is an essential feature of any physics engine or robot motion planner. It has often been tackled as a computational geometry problem, with the Gilbert, Johnson and Keerthi (GJK) algorithm being the most common approach today. In this work we leverage the fact that collision detection is fundamentally a convex optimization problem. In particular, we establish that the GJK algorithm is a specific sub-case of the well-established Frank-Wolfe (FW) algorithm in convex optimization. We introduce a new collision detection algorithm by adapting recent works linking Nesterov acceleration and Frank-Wolfe methods. We benchmark the proposed accelerated collision detection method on two datasets composed of strictly convex and non-strictly convex shapes. Our results show that our approach significantly reduces the number of iterations to solve collision detection problems compared to the state-of-the-art GJK algorithm, leading to up to two times faster computation times. © 2022, MIT Press Journals. All rights reserved.

关键词： Computational geometry

Parameter Adjustments in POMDP-Based Trajectory Planning for Unsignalized Intersections

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arXiv 2024年

作者： Kollarčík, Adam Hanzálek, Zdeněk Dept. of Control Engineering Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic

This paper investigates the problem of trajectory planning for autonomous vehicles at unsignalized intersections, specifically focusing on scenarios where the vehicle lacks the right of way and yet must cross safely. To address this issue, we have employed a method based on the Partially Observable Markov Decision Processes (POMDPs) framework designed for planning under uncertainty. The method utilizes the Adaptive Belief Tree (ABT) algorithm as an approximate solver for the POMDPs. We outline the POMDP formulation, beginning with discretizing the intersection’s topology. Additionally, we present a dynamics model for the prediction of the evolving states of vehicles, such as their position and velocity. Using an observation model, we also describe the connection of those states with the imperfect (noisy) available measurements. Our results confirmed that the method is able to plan collision-free trajectories in a series of simulations utilizing real-world traffic data from aerial footage of two distinct intersections. Furthermore, we studied the impact of parameter adjustments of the ABT algorithm on the method’s performance. This provides guidance in determining reasonable parameter settings, which is valuable for future method applications. © 2024, CC BY-NC-ND.

关键词： Markov processes

A Guide to Structureless Visual Localization

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arXiv 2025年

作者： Panek, Vojtech Zhou, Qunjie Ding, Yaqing Agostinho, Sérgio Kukelova, Zuzana Sattler, Torsten Leal-Taixé, Laura in Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics CTU in Prague Czech Republic NVIDIA United States Visual Recognition Group Faculty of Electrical Engineering CTU in Prague Czech Republic

Visual localization algorithms, i.e., methods that estimate the camera pose of a query image in a known scene, are core components of many applications, including self-driving cars and augmented / mixed reality systems. State-of-the-art visual localization algorithms are structure-based, i.e., they store a 3D model of the scene and use 2D-3D correspondences between the query image and 3D points in the model for camera pose estimation. While such approaches are highly accurate, they are also rather inflexible when it comes to adjusting the underlying 3D model after changes in the scene. Structureless localization approaches represent the scene as a database of images with known poses and thus offer a much more flexible representation that can be easily updated by adding or removing images. Although there is a large amount of literature on structure-based approaches, there is significantly less work on structureless methods. Hence, this paper is dedicated to providing the, to the best of our knowledge, first comprehensive discussion and comparison of structureless methods. Extensive experiments show that approaches that use a higher degree of classical geometric reasoning generally achieve higher pose accuracy. In particular, approaches based on classical absolute or semi-generalized relative pose estimation outperform very recent methods based on pose regression by a wide margin. Compared with state-of-the-art structure-based approaches, the flexibility of structureless methods comes at the cost of (slightly) lower pose accuracy, indicating an interesting direction for future work. Copyright © 2025, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Robotic Grasping of Harvested Tomato Trusses Using Vision and Online Learning

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arXiv 2023年

作者： Bent, Luuk van den Coleman, Tomás Babuška, Robert Department of Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic

Currently, truss tomato weighing and packaging require significant manual work. The main obstacle to automation lies in the difficulty of developing a reliable robotic grasping system for already harvested trusses. We propose a method to grasp trusses that are stacked in a crate with considerable clutter, which is how they are commonly stored and transported after harvest. The method consists of a deep learning-based vision system to first identify the individual trusses in the crate and then determine a suitable grasping location on the stem. To this end, we have introduced a grasp pose ranking algorithm with online learning capabilities. After selecting the most promising grasp pose, the robot executes a pinch grasp without needing touch sensors or geometric models. Lab experiments with a robotic manipulator equipped with an eye-in-hand RGB-D camera showed a 100% clearance rate when tasked to pick all trusses from a pile. 93% of the trusses were successfully grasped on the first try, while the remaining 7% required more attempts. Copyright © 2023, The Authors. All rights reserved.

关键词： Trusses

POMDP-Based Trajectory Planning for On-Ramp Highway Merging

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arXiv 2024年

This paper addresses the trajectory planning problem for automated vehicle on-ramp highway merging. To tackle this challenge, we extend our previous work on trajectory planning at unsignalized intersections using Partially Observable Markov Decision Processes (POMDPs). The method utilizes the Adaptive Belief Tree (ABT) algorithm, an approximate sampling-based approach to solve POMDPs efficiently. We outline the POMDP formulation process, beginning with discretizing the highway topology to reduce problem complexity. Additionally, we describe the dynamics and measurement models used to predict future states and establish the relationship between available noisy measurements and predictions. Building on our previous work, the dynamics model is expanded to account for lateral movements necessary for lane changes during the merging process. We also define the reward function, which serves as the primary mechanism for specifying the desired behavior of the automated vehicle, combining multiple goals such as avoiding collisions or maintaining appropriate velocity. Our simulation results, conducted on three scenarios based on real-life traffic data from German highways, demonstrate the method’s ability to generate safe, collision-free, and efficient merging trajectories. This work shows the versatility of this POMDP-based approach in tackling various automated driving problems. © 2024, CC BY-NC-ND.

关键词： Markov processes

Dynamical phase transitions in graph cellular automata

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Physical Review E 2024年第4期109卷 044312-044312页

作者： Freya Behrens Barbora Hudcová Lenka Zdeborová Statistical Physics Of Computation Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland Algebra Department Faculty of Mathematics and Physics Charles University Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic

Discrete dynamical systems can exhibit complex behavior from the iterative application of straightforward local rules. A famous class of examples comes from cellular automata whose global dynamics are notoriously challenging to analyze. To address this, we relax the regular connectivity grid of cellular automata to a random graph, which gives the class of graph cellular automata. Using the dynamical cavity method and its backtracking version, we show that this relaxation allows us to derive asymptotically exact analytical results on the global dynamics of these systems on sparse random graphs. Concretely, we showcase the results on a specific subclass of graph cellular automata with “conforming nonconformist” update rules, which exhibit dynamics akin to opinion formation. Such rules update a node's state according to the majority within their own neighborhood. In cases where the majority leads only by a small margin over the minority, nodes may exhibit nonconformist behavior. Instead of following the majority, they either maintain their own state, switch it, or follow the minority. For configurations with different initial biases towards one state we identify sharp dynamical phase transitions in terms of the convergence speed and attractor types. From the perspective of opinion dynamics this answers when consensus will emerge and when two opinions coexist almost indefinitely.

关键词： Dynamical phase transitions