Multi-Target Maneuver Coordinations: Unlocking Coordination Opportunities in Connected Automated Driving
- URL: http://arxiv.org/abs/2606.22055v1
- Date: Sat, 20 Jun 2026 14:14:19 GMT
- Title: Multi-Target Maneuver Coordinations: Unlocking Coordination Opportunities in Connected Automated Driving
- Abstract summary: This paper introduces a maneuver coordination approach based on multi-target selection.<n>It allows a vehicle to identify and select among multiple potential coordination vehicles for a given maneuver.<n>Results show that multi-target maneuver coordination significantly increases triggered and successfully executed coordinations.
- Score: 6.284195585770223
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Maneuver coordination is a key enabler of connected and automated driving, allowing vehicles to negotiate and execute maneuvers that would otherwise be difficult, inefficient or unsafe. Existing approaches and use cases typically assume coordination with a single predefined target vehicle, which limits the number of coordination opportunities. This paper introduces a maneuver coordination approach based on multi-target selection, which allows a vehicle to identify and select among multiple potential coordination vehicles for a given maneuver. Multi-target maneuver coordination does not require modifications to the maneuver execution logic or to the underlying coordination protocol. Instead, it extends the decision-making process preceding coordination, enabling vehicles to exploit a broader set of feasible cooperative interactions. Results show that multi-target maneuver coordination significantly increases triggered and successfully executed coordinations while maintaining a low computational cost, as the proposed approach achieves these gains without requiring the analysis of a large number of potential target vehicles. These improvements preserve coordination success rates while enabling earlier maneuver initiation.
Related papers
- Importance of Intent-Sharing for V2X-based Maneuver Coordination [5.02193210755999]
Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle.<n>We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario.<n>Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles' driving intentions.
arXiv Detail & Related papers (2026-06-23T06:42:53Z) - HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds [24.47863400717969]
We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems.<n>Our architecture systematically decouples high-level collaborative reasoning from low-level robust motion execution.<n>Results demonstrate reliable task execution, strict configurationism, and exceptional resilience against severe physical perturbations.
arXiv Detail & Related papers (2026-05-17T07:23:03Z) - SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy [59.86199649493784]
Controllable cooperative humanoid manipulation is a fundamental yet challenging problem for embodied intelligence.<n>We present SynAgent, a framework that enables scalable and physically plausible cooperative manipulation.<n>In experiments, SynAgent significantly outperforms existing baselines in both cooperative imitation and trajectory-conditioned control.
arXiv Detail & Related papers (2026-04-20T17:46:20Z) - DeCoNav: Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation [51.978605314711835]
Long-horizon collaborative vision-language navigation (VLN) is critical for multi-robot systems to accomplish complex tasks beyond the capability of a single agent.<n>We present Dialog enhanced Long-Horizon Collaborative Vision-Language Navigation (DeCoNav)<n>DeCoNav is a decentralized framework that couples event-triggered dialogue with dynamic task allocation and replanning for real-time, adaptive coordination.
arXiv Detail & Related papers (2026-04-14T09:11:55Z) - TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size [54.0714652192002]
Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors.<n>We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents.
arXiv Detail & Related papers (2026-03-09T05:52:13Z) - A Human-Oriented Cooperative Driving Approach: Integrating Driving Intention, State, and Conflict [50.983743505985956]
We propose a Human-Oriented Cooperative Driving (HOCD) approach to establish more natural and effective human-vehicle interaction.<n>In implementation, we take both tactical and operational levels into account to ensure seamless human-vehicle cooperation.<n>Compared to other cooperative driving approaches, the proposed HOCD approach significantly enhances driving performance and mitigates human-machine conflict.
arXiv Detail & Related papers (2025-12-29T05:51:00Z) - CooT: Learning to Coordinate In-Context with Coordination Transformers [10.888155149916967]
Coordination Transformers (coot) is a novel in-context coordination framework that rapidly adapts to unseen partners.<n>coot consistently outperforms baselines including population-based approaches, gradient-based fine-tuning, and a Meta-RL-inspired contextual adaptation method.<n>By contrast, coot achieves stable, rapid in-context adaptation and is consistently ranked the most effective collaborator in human evaluations.
arXiv Detail & Related papers (2025-06-30T06:45:39Z) - CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation [98.11670473661587]
CaPo improves cooperation efficiency with two phases: 1) meta-plan generation, and 2) progress-adaptive meta-plan and execution.<n> Experimental results on the ThreeDworld Multi-Agent Transport and Communicative Watch-And-Help tasks demonstrate that CaPo achieves much higher task completion rate and efficiency compared with state-of-the-arts.
arXiv Detail & Related papers (2024-11-07T13:08:04Z) - Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [87.7482313774741]
Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory.<n>This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
arXiv Detail & Related papers (2024-09-19T14:36:00Z) - Real-time Cooperative Vehicle Coordination at Unsignalized Road
Intersections [7.860567520771493]
Cooperative coordination at unsignalized road intersections aims to improve the safety driving traffic throughput for connected and automated vehicles.
We introduce a model-free Markov Decision Process (MDP) and tackle it by a Twin Delayed Deep Deterministic Policy (TD3)-based strategy in the deep reinforcement learning framework.
We show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve control in the realistic continuous flow.
arXiv Detail & Related papers (2022-05-03T02:56:02Z)
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.