V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach
- URL: http://arxiv.org/abs/2604.22196v1
- Date: Fri, 24 Apr 2026 03:55:32 GMT
- Title: V-STC: A Time-Efficient Multi-Vehicle Coordinated Trajectory Planning Approach
- Abstract summary: This paper presents a new approach, termed variable time-step-vehicle corridor (STC-STC)<n>An optimization model is formulated to construct a V-STC for each autonomous vehicle (AV)<n>By allowing the corridor's spatial position and time step to vary, the constructed V-STC reduces the overall temporal occupancy of each AV.<n>The method achieves safe multi-vehicle coordination and yields more time-efficient motion compared with existing STC approaches.
- Score: 63.475658003162266
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Coordinating the motions of multiple autonomous vehicles (AVs) requires planning frameworks that ensure safety while making efficient use of space and time. This paper presents a new approach, termed variable-time-step spatio-temporal corridor (V-STC), that enhances the temporal efficiency of multi-vehicle coordination. An optimization model is formulated to construct a V-STC for each AV, in which both the spatial configuration of the corridor cubes and their time durations are treated as decision variables. By allowing the corridor's spatial position and time step to vary, the constructed V-STC reduces the overall temporal occupancy of each AV while maintaining collision-free separation in the spatio-temporal domain. Based on the generated V-STC, a dynamically feasible trajectory is then planned independently for each AV. Simulation studies demonstrate that the proposed method achieves safe multi-vehicle coordination and yields more time-efficient motion compared with existing STC approaches.
Related papers
- Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets [4.512110686124693]
We present an algorithmic framework based on graphs of space-time convex sets (ST-GCSs)<n>For multi-robot motion planning, we integrate ST-GCS planning and ECD into prioritized planning methods.<n>For large-scale demonstrations, the proposed multi-robot motion planner can solve instances with up to $100$ robots within only a few minutes.
arXiv Detail & Related papers (2026-07-01T04:58:00Z) - Towards Physically Consistent 4D Scene Reconstruction for Closed-loop Autonomous Driving Simulation [19.055975606481635]
High-fidelity street scene reconstruction is pivotal for end-to-end autonomous driving simulation.<n>Existing 3DGS methods and their 4D extensions fail to simultaneously achieve both.<n>We propose Orthogonal Projected Gradient (OPG) to restore spatial identifiability.
arXiv Detail & Related papers (2026-05-20T11:09:53Z) - RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework [49.531001563853984]
RAD-2 is a unified generator-discriminator framework for closed-loop planning.<n>It produces diverse trajectory candidates, while an RL-optimized discriminator reranks these candidates according to their long-term driving quality.<n>It reduces the collision rate by 56% compared with strong diffusion-based planners.
arXiv Detail & Related papers (2026-04-16T17:59:44Z) - Accelerated Spline-Based Time-Optimal Motion Planning with Continuous Safety Guarantees for Non-Differentially Flat Systems [6.862015448582809]
State-of-the-art approaches formulate spline-based motion planning as a single Control Problem (OCP)<n>By treating separation as an independent classification problem solvable via a linear system or quadratic program, the proposed method eliminates hyperplane parameters from the OCP variables.<n> Experimental validation demonstrates that this decoupled approach reduces trajectory times up to almost 60% compared to fully-rich environments.
arXiv Detail & Related papers (2026-03-25T09:53:04Z) - Fast Near Time-Optimal Motion Planning for Holonomic Vehicles in Structured Environments [8.195916347194315]
The approach aims to solve the problem of motion planning for planar motion systems using magnetic levitation.<n>The presented approach encodes the environment representation using free-space corridors and represents the motion of the vehicle through such a corridor using a motion primitive.<n>The method achieves significantly lower times compared to the state-of-the-art and is validated on a real-world Beckhoff XPlanar system.
arXiv Detail & Related papers (2026-02-02T21:26:30Z) - Optimizing Multi-Lane Intersection Performance in Mixed Autonomy Environments [0.0]
This paper presents a novel traffic signal control framework that combines Graph Attention Networks (GAT) with Soft Actor-Critic (SAC) reinforcement learning to address this challenge.<n>The proposed SAC is a robust off-policy reinforcement learning algorithm that enables adaptive signal control through entropy-optimized decision making.<n>The experimental results demonstrate the effectiveness of the GAT-SAC approach by achieving a 24.1% reduction in average delay and up to 29.2% fewer traffic violations compared to traditional methods.
arXiv Detail & Related papers (2025-11-04T03:10:47Z) - Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
arXiv Detail & Related papers (2025-10-28T10:05:53Z) - B-TGAT: A Bi-directional Temporal Graph Attention Transformer for Clustering Multivariate Spatiotemporal Data [0.15763762230817438]
We present a time-distributed hybrid U-Net autocoder that integrates a Bi-multidimensional Graph Attention Transformer (B-TGAT) to guide efficient temporal clustering.<n>The encoder and decoder are equipped with ConvLSTM2D modules that extract joint spatial-temporal features.<n> experiments show superior cluster separability, temporal stability, and alignment with known climate transitions compared to state-of-the-art baselines.
arXiv Detail & Related papers (2025-09-16T16:08:21Z) - Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets [6.0967385124149756]
Agile planning using existing non-linear model predictive control methods is limited by the number of planning steps as it becomes increasingly demanding.<n>In this paper, we propose to address these limitations by introducing variable time steps and coupling them with the prediction horizon length.<n>A simplified point-mass motion primitive is used to leverage the differential flatness of quadrotor dynamics and the trajectory generation of feasible trajectories in the flat output space.
arXiv Detail & Related papers (2025-03-18T11:59:24Z) - A Decoupled Spatio-Temporal Framework for Skeleton-based Action
Segmentation [89.86345494602642]
Existing methods are limited in weak-temporal modeling capability.
We propose a Decoupled Scoupled Framework (DeST) to address the issues.
DeST significantly outperforms current state-of-the-art methods with less computational complexity.
arXiv Detail & Related papers (2023-12-10T09:11:39Z) - Federated Learning on the Road: Autonomous Controller Design for
Connected and Autonomous Vehicles [109.71532364079711]
A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs)
A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs.
A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
arXiv Detail & Related papers (2021-02-05T19:57:47Z) - Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting.
Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies.
The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
arXiv Detail & Related papers (2020-01-09T10:21:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.