FleetAgent: Teleoperation Assistant for Autonomous Fleets via Vectorized V2N Messages
For operators of large-scale autonomous fleets, FleetAgent enables efficient and explainable teleoperation monitoring by replacing raw sensor data with compact structured messages, addressing the scalability bottleneck of streaming high-bandwidth data.
FleetAgent introduces a cloud-hosted MLLM that uses compact vectorized V2N messages to assist teleoperation for autonomous fleets, reducing uplink payload by 625x and KV-cache memory by 16.54x, while improving Lingo-Judge score by 16.8% and reducing intervention failure rate by 19.9% on the VecEval dataset.
Large-scale autonomous fleets rely on teleoperation to resolve rare failures, yet streaming raw sensor data from many vehicles is costly, and remote operators can only monitor a limited number of vehicles at a time. We introduce FleetAgent, a cloud-hosted multimodal large language model (MLLM) assistant that consumes compact vectorized vehicle-to-network (V2N) messages, such as map elements, detected objects, and the ego planned path. It provides a structured natural-language response (including narration, explanation, and evaluation of the plan and scene), along with an intervention urgency score for operator prioritization. To make structured messages compatible with token-based MLLMs, we propose VecFormer, a vector-to-embedding interface with differentiable top-K context selection that bounds context length and GPU KV-cache growth, enabling more efficient batch processing, which is important under the context of cloud-hosted large-scale fleet management. We also construct VecEval, a nuScenes-derived dataset with paired human and synthetic imperfect plans and human-verified language labels, to facilitate the training and evaluation of our proposed system. Our proposed system can reduce uplink payload by up to 625 times compared with raw images and reduce KV-cache memory by 16.54 times compared with original text descriptions. On VecEval, FleetAgent improves Lingo-Judge score by 16.8% and reduces intervention failure rate by 19.9%, compared with Qwen2.5-VL-7B using language descriptions. These results demonstrate that FleetAgent can utilize compact structured V2N messaging to enable efficient, explainable teleoperation monitoring for autonomous fleets.