Results 161 to 170 of about 23,271 (259)

Personalized Shared Control for Automated Vehicles Considering Driving Capability and Styles. [PDF]

Sensors (Basel)
Sun B, Shan Y, Wu G, Zhao S, Xie F.
europepmc   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

Exploring the general acceptance factor for shared automated vehicles: the impact of personality traits and experimentally altered information. [PDF]

Front Psychol
Aasvik O, Ulleberg P, Hagenzieker M.
europepmc   +1 more source

Multimodal interface and reliability displays: Effect on attention, mode awareness, and trust in partially automated vehicles. [PDF]

Front Psychol, 2023
Monsaingeon N, Caroux L, Langlois S, Lemercier C.   +3 more
europepmc   +1 more source

Characteristics, Management, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi

Advanced Intelligent Systems, EarlyView.
Musculoskeletal humanoids exhibit rich biomechanical properties that remain insufficiently unified in prior discussions. This article systematically categorizes muscle characteristics into five properties: redundancy, independency, anisotropy, variable moment arm, and nonlinear elasticity, and analyzes their combined effects on control.
Kento Kawaharazuka, Kei Okada, Masayuki Inaba   +2 more
wiley   +1 more source

Triboelectric sensor gloves for real-time behavior identification and takeover time adjustment in conditionally automated vehicles. [PDF]

Nat Commun
Lu X, Tan H, Zhang H, Wang W, Xie S, Yue T, Chen F.   +6 more
europepmc   +1 more source

Using System Dynamics Approach to Explore the Mode Shift between Automated Vehicles, Conventional Vehicles, and Public Transport in Melbourne, Australia. [PDF]

Sensors (Basel), 2023
Chen Y, Stasinopoulos P, Shiwakoti N, Khan SK.   +3 more
europepmc   +1 more source

Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

Advanced Intelligent Systems, EarlyView.
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer, Bassam Alrifaee, Ehsan Hashemi   +2 more
wiley   +1 more source

Editorial: Automated vehicles: intelligent decision-making, trajectory planning, and chassis execution. [PDF]

Front Robot AI
Cheng S, Li L, Lei Y, Xia X, Lv C.
europepmc   +1 more source

V2X Communication between Connected and Automated Vehicles (CAVs) and Unmanned Aerial Vehicles (UAVs). [PDF]

Sensors (Basel), 2022
Kavas-Torris O, Gelbal SY, Cantas MR, Aksun Guvenc B, Guvenc L.   +4 more
europepmc   +1 more source