FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages

FleetAgent is a cloud-based AI system that uses compact vectorized vehicle-to-network messages to assist remote operators in managing autonomous vehicle fleets. The system reduces data transmission costs by up to 625x compared to raw images while improving teleoperation monitoring accuracy and decision-making efficiency.

FleetAgent addresses a critical infrastructure challenge in autonomous fleet management: the bandwidth and scalability constraints of real-time teleoperation. Remote operators managing large fleets traditionally require streaming raw sensor data from multiple vehicles simultaneously, creating prohibitive computational and network costs. This research proposes a practical solution by intermediating sensor data through a multimodal language model that processes vectorized V2N messages rather than raw inputs, fundamentally changing how fleet monitoring infrastructure can scale.

The autonomous vehicle industry has long recognized teleoperation as essential for handling edge cases and rare failure modes that fully autonomous systems cannot resolve independently. However, the economics of teleoperation have remained challenging—bandwidth costs and operator attention bottlenecks limit fleet sizes. FleetAgent's vectorization approach compresses payloads dramatically while the VecFormer architecture addresses GPU memory constraints critical for cloud-hosted operations, making large-scale teleoperation economically viable.

The performance metrics demonstrate tangible improvements: 16.8% higher scoring on language evaluation metrics and 19.9% lower intervention failure rates compared to baseline vision-language models. These gains suggest operators can make faster, more accurate decisions with less information, directly reducing fleet downtime and operational costs. The VecEval dataset contribution also establishes evaluation standards for this emerging problem space.

Looking ahead, the real test involves real-world fleet deployment. Success depends on whether vectorized representations preserve sufficient situational awareness for safe operator interventions, and whether the system generalizes beyond the nuScenes dataset to diverse driving conditions and failure modes. Integration with existing fleet management platforms will determine market adoption.

• →FleetAgent reduces teleoperation uplink payload by 625x through vectorized V2N messages, dramatically lowering infrastructure costs for autonomous fleet management.
• →VecFormer's differentiable top-K context selection reduces KV-cache memory requirements by 16.54x, enabling efficient cloud-hosted batch processing at scale.
• →The system improves operator decision accuracy by 16.8% on language evaluation and reduces intervention failures by 19.9% compared to vision-language baselines.
• →VecEval dataset provides the first standardized benchmark for evaluating multimodal teleoperation systems on autonomous vehicles, establishing evaluation methodology.
• →The approach balances compression efficiency with situational awareness, suggesting vectorized representations can replace raw sensor streaming for remote fleet supervision.