检索结果-内蒙古大学图书馆

Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Golchoubian, Mahsa Ghafurian, Moojan Dautenhahn, Kerstin Azad, Nasser Lashgarian Department of Systems Design Engineering University of Waterloo Canada Department of Electrical and Computer Engineering University of Waterloo Canada

Predicting pedestrians’ trajectories is a crucial capability for autonomous vehicles’ safe navigation, especially in spaces shared with pedestrians. Pedestrian motion in shared spaces is influenced by both the presence of vehicles and other pedestrians. Therefore, effectively modelling both pedestrian-pedestrian and pedestrian-vehicle interactions can increase the accuracy of the pedestrian trajectory prediction models. Despite the huge literature on ways to encode the effect of interacting agents on a pedestrian’s predicted trajectory using deep-learning models, limited effort has been put into the effective selection of interacting agents. In the majority of cases, the interaction features used are mainly based on relative distances while paying less attention to the effect of the velocity and approaching direction in the interaction formulation. In this paper, we propose a heuristic-based process of selecting the interacting agents based on collision risk calculation. Focusing on interactions of potentially colliding agents with a target pedestrian, we propose the use of time-to-collision and the approach direction angle of two agents for encoding the interaction effect. This is done by introducing a novel polar collision grid map. Our results have shown predicted trajectories closer to the ground truth compared to existing methods (used as a baseline) on the HBS dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks

学校读者我要写书评

暂无评论

Polar Collision Grids: Effective Interaction Modelling for P...

International Conference on Intelligent Transportation

作者： Mahsa Golchoubian Moojan Ghafurian Kerstin Dautenhahn Nasser Lashgarian Azad Department of Systems Design Engineering University of Waterloo Canada Department of Electrical and Computer Engineering University of Waterloo Canada

Predicting pedestrians' trajectories is a crucial capability for autonomous vehicles' safe navigation, especially in spaces shared with pedestrians. Pedestrian motion in shared spaces is influenced by both the presence of vehicles and other pedestrians. Therefore, effectively modelling both pedestrian-pedestrian and pedestrian-vehicle interactions can increase the accuracy of the pedestrian trajectory prediction models. Despite the huge literature on ways to encode the effect of interacting agents on a pedestrian's predicted trajectory using deep-learning models, limited effort has been put into the effective selection of interacting agents. In the majority of cases, the interaction features used are mainly based on relative distances while paying less attention to the effect of the velocity and approaching direction in the interaction formulation. In this paper, we propose a heuristic-based process of selecting the interacting agents based on collision risk calculation. Focusing on interactions of potentially colliding agents with a target pedestrian, we propose the use of time-to-collision and the approach direction angle of two agents for encoding the interaction effect. This is done by introducing a novel polar collision grid map. Our results have shown predicted trajectories closer to the ground truth compared to existing methods (used as a baseline) on the HBS dataset.

关键词：

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

学校读者我要写书评

暂无评论

arXiv 2023年

Planning an autonomous vehicle’s (AV) path in a space shared with pedestrians requires reasoning about pedestrians’ future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle’s interactions with the pedestrians on pedestrians’ future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

Efficient Multi-user Offloading of Personalized Diffusion Models: A DRL-Convex Hybrid Solution

学校读者我要写书评

暂无评论

IEEE Transactions on Mobile Computing 2025年

作者： Yang, Wanting Xiong, Zehui Guo, Song Mao, Shiwen Kim, Dong In Debbah, Merouane Singapore University of Technology and Design Pillar of Engineering Systems and Design Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong Auburn University Department of Electrical and Computer Engineering Auburn36830 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Khalifa University KU 6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Generative diffusion models like Stable Diffusion are at the forefront of the thriving field of generative models today, celebrated for their robust training methodologies and high-quality photorealistic generation capabilities. These models excel in producing rich content, establishing them as essential tools in the industry. Building on this foundation, the field has seen the rise of personalized content synthesis as a particularly exciting application. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with heterogeneous computational power. To address this, we propose a novel framework for efficient multi-user offloading of personalized diffusion models. This framework accommodates a variable number of users, each with different computational capabilities, and adapts to the fluctuating computational resources available on edge servers. To enhance computational efficiency and alleviate the storage burden on edge servers, we propose a tailored multi-user hybrid inference approach. This method splits the inference process for each user into two phases, with an optimizable split point. Initially, a cluster-wide model processes low-level semantic information for each user's prompt using batching techniques. Subsequently, users employ their personalized models to refine these details during the later phase of inference. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that balances both latency and accuracy across all users. To solve this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining dee

关键词： Markov processes

Accelerating OTA Circuit design: Transistor Sizing Based on a Transformer Model and Precomputed Lookup Tables

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Ghosh, Subhadip Gebru, Endalk Y. Kashyap, Chandramouli V. Harjani, Ramesh Sapatnekar, Sachin S. Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Cadence Design Systems PortlandOR United States

Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the driving-point signal flow graph (DPSFG) to map an OTA circuit and its specifications into transformer-friendly sequential data. A specialized tokenization approach is applied to the sequential data to expedite the training of the transformer on a diverse range of OTA topologies, under multiple specifications. Under specific performance constraints, the trained transformer model is used to accurately predict DP-SFG parameters in the inference phase. The predicted DP-SFG parameters are then translated to transistor sizes using a precomputed look-up table-based approach inspired by the gm/Id methodology. In contrast to previous conventional or machine-learning-based methods, the proposed framework achieves significant improvements in both speed and computational efficiency by reducing the need for expensive SPICE simulations within the optimization loop;instead, almost all SPICE simulations are confined to the onetime training phase. The method is validated on a variety of unseen specifications, and the sizing solution demonstrates over 90% success in meeting specifications with just one SPICE simulation for validation, and 100% success with 3–5 additional SPICE simulations. © 2025, CC BY.

关键词： Table lookup

Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse

学校读者我要写书评

暂无评论

Generative AI-empowered Effective Physical-Virtual Synchroni...

2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Xu, Minrui Niyato, Dusit Zhang, Hongliang Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Peking University School of Electronics Beijing100871 China Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849-5201 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

ISBN: (纸本)9798350333336

Metaverse seamlessly blends the physical world and virtual spaces through ubiquitous communication and computing equipment and infrastructure. In intelligent transportation systems, the vehicular Metaverse can provide a fully immersive and hyperreal travel experience (e.g., via augmented reality head-up displays, AR-HUDs) to drivers and passengers in autonomous vehicles (AVs) through roadside units (RSUs). However, providing real-time and immersive services requires effective physical-virtual synchronization between AVs and virtual simulators. This paper proposes a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In physical-to-virtual synchronization, digital twin (DT) tasks generated by AVs are offloaded for execution in RSUs with future route generation. In virtual-to-physical synchronization, virtual simulators customize diverse and personalized AR content via generative AI models based on user preferences. Furthermore, we propose a multi-task enhanced auction-based mechanism to match and price AVs and virtual simulators for RSUs to provide real-time and effective services. Finally, property analysis and experimental results demonstrate that the proposed mechanism is strategy-proof and adverse-selection free while increasing social surplus. © 2023 IEEE.

关键词： Augmented reality

Robust Online Epistemic Replanning of Multi-Robot Missions

学校读者我要写书评

暂无评论

Robust Online Epistemic Replanning of Multi-Robot Missions

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Lauren Bramblett Branko Miloradović Patrick Sherman Alessandro V. Papadopoulos Nicola Bezzo Departments of Systems and Information Engineering and Electrical and Computer Engineering University of Virginia Charlottesville VA USA Division of Intelligent Future Technologies Mälardalen University Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

As Multi-Robot systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles.

关键词： Uncertainty Heuristic algorithms Search problems Regulation Planning Space exploration Resource management Multi-robot systems Vehicle dynamics Belief propagation

Throughput Maximizing Takeoff Scheduling for eVTOL Vehicles in On-Demand Urban Air Mobility systems

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Pooladsanj, Milad Savla, Ketan Ioannou, Petros A. The Department of Electrical Engineering University of Southern California Los AngelesCA90007 United States The Departments of Civil and Environmental Engineering Electrical and Computer Engineering and the Industrial and Systems Engineering University of Southern California Los AngelesCA90089 United States

Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for conflict-free takeoff scheduling of eVTOLs in on-demand UAM systems. Specifically, we provide a scheduling policy, called VertiSync, which jointly schedules UAM vehicles for servicing trip requests and rebalancing, subject to safety margins and energy requirements. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which the average waiting time transitions from being stable to being increasing over time. We show that the proposed policy maximizes throughput for sufficiently large fleet size and if the UAM network has a certain symmetry property. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles, and show that it significantly reduces average passenger waiting time compared to a first-come first-serve scheduling policy. Copyright © 2025, The Authors. All rights reserved.

关键词： Traffic congestion

Digital FIR Boosting to Correct IRLED RIIC Analog Delay 5

学校读者我要写书评

暂无评论

Digital FIR Boosting to Correct IRLED RIIC Analog Delay

5th IEEE Research and Applications of Photonics in Defense Conference, RAPID 2022

作者： Browning, Tyler Greenlee, Matt Joyce, Michael Landwehr, Aaron Kiamilev, Fouad Chip Design Systems NewarkDE19702 United States University of Delaware Department of Electrical and Computer Engineering NewarkDE19702 United States

Over the past year, CDS IRSP systems have seen many improvements, and the next generation of technology is under development. In preparation for the next-generation platform, several regressions were identified to be ... 详细信息

ISBN: (数字)9781665402231

ISBN: (纸本)9781665402231

关键词： FIR filters