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This paper introduces MIND-CAVs, a Multi-Intelligence Negotiation and Decision framework designed to enhance coordination among connected autonomous vehicles (CAVs) by enabling the exchange of high-level maneuver intentions. By abstracting raw sensor data into structured intent representations and utilizing edge computing for decision arbitration, the framework allows vehicles to negotiate conflicting intents and receive globally consistent coordination plans. Experimental evaluations demonstrate that MIND-CAVs significantly improve maneuver completion times and reduce unsafe interactions compared to traditional isolated autonomy and existing cooperative frameworks.
MIND-CAVs transform the landscape of connected autonomous vehicle coordination by enabling intent-driven negotiations that enhance safety and efficiency on the road.
Modern autonomous vehicles largely operate as isolated agents: they rely on on-board perception and decision modules and broadcast Basic Safety Messages (BSMs) that expose only low-level kinematic state. While existing cooperative driving frameworks enable limited sensor sharing, they rarely communicate high-level maneuver intentions, and edge computing is primarily used for content delivery rather than decision arbitration. As a result, current connected autonomy lacks a principled mechanism for making globally consistent, intent-aware coordination decisions across vehicles. To address this gap, we propose MIND-CAVs, a Multi-Intelligence Negotiation and Decision framework for connected autonomous vehicles (CAVs) based on intent-driven autonomy. Each vehicle abstracts raw sensor observations into structured intent representations, exchanges them over V2X links, and receives globally consistent coordination plans from roadside edge servers. Edge agents combine learned and rule-based arbitration mechanisms to negotiate conflicting intents among vehicles, while a cloud platform records decisions for auditing and continual retraining. We implement MIND-CAVs in a CARLA-based AI-in-the-loop platform and evaluate it in multi-lane highway scenarios involving conflicting maneuvers and route-constrained exits. Experimental results show improved maneuver completion time and reduced unsafe proximity and unnecessary braking compared with isolated autonomy, first-come-first-served arbitration, and multi-agent reinforcement learning baselines.