# MMCD: Multi-Modal Collaborative Decision-Making for Connected Autonomy with Knowledge Distillation arXiv: https://arxiv.org/abs/2509.18198 ยท HTML: https://mlsanity.com/paper/2509.18198/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Placed in Multimodal distillation, one of 12 directions in Knowledge distillation. - Builds on 33 of its 44 references that are in the map. - First posted on arXiv 19 Sep 2025. ## Placed in - Knowledge distillation / [Multimodal distillation](https://mlsanity.com/distill/multimodal-distillation/) ## Builds on (most cited first, 30 of 33) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020) - [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020) - [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017) - [Patient Knowledge Distillation for BERT Model Compression](https://mlsanity.com/paper/1908.09355/) (2019) - [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024) - [Knowledge distillation: A good teacher is patient and consistent](https://mlsanity.com/paper/2106.05237/) (2021) - [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022) - [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015) - [When2com: Multi-Agent Perception via Communication Graph Grouping](https://mlsanity.com/paper/2006.00176/) (2020) - [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022) - [Who2com: Collaborative Perception via Learnable Handshake Communication](https://mlsanity.com/paper/2003.09575/) (2020) - [Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data](https://mlsanity.com/paper/2203.16258/) (2022) - [Multimodal End-to-End Autonomous Driving](https://mlsanity.com/paper/1906.03199/) (2019) - [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020) - [COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles](https://mlsanity.com/paper/2205.02222/) (2022) - [Self-Knowledge Distillation in Natural Language Processing](https://mlsanity.com/paper/1908.01851/) (2019) - [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019) - [RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/1906.00208/) (2019) - [AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving](https://mlsanity.com/paper/2112.14947/) (2021) - [A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning](https://mlsanity.com/paper/1707.02515/) (2017) - [itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection](https://mlsanity.com/paper/2205.15531/) (2022) - [LAVA: Long-horizon Visual Action based Food Acquisition](https://mlsanity.com/paper/2403.12876/) (2024) - [Adaptive Visual Imitation Learning for Robotic Assisted Feeding Across Varied Bowl Configurations and Food Types](https://mlsanity.com/paper/2403.12891/) (2024) - [Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis](https://mlsanity.com/paper/2403.08955/) (2024) - [CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems](https://mlsanity.com/paper/2502.17821/) (2025) - [Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours](https://mlsanity.com/paper/2103.07910/) (2021) - [Data-Driven Distributionally Robust Optimal Control with State-Dependent Noise](https://mlsanity.com/paper/2303.02293/) (2023) ## Built on by (most cited first, 2 of 2) - [Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty](https://mlsanity.com/paper/2602.04763/) (2026) - [Align Then Reason: A Multimodal Lip-Sync Judge for Dubbing](https://mlsanity.com/paper/2610.00825/) (2026) ## Abstract (arXiv) Autonomous systems have advanced significantly, but challenges persist in accident-prone environments where robust decision-making is crucial. A single vehicle's limited sensor range and obstructed views increase the likelihood of accidents. Multi-vehicle connected systems and multi-modal approaches, leveraging RGB images and LiDAR point clouds, have emerged as promising solutions. However, existing methods often assume the availability of all data modalities and connected vehicles during both training and testing, which is impractical due to potential sensor failures or missing connected vehicles. To address these challenges, we introduce a novel framework MMCD (Multi-Modal Collaborative Decision-making) for connected autonomy. Our framework fuses multi-modal observations from ego and collaborative vehicles to enhance decision-making under challenging conditions. To ensure robust performance when certain data modalities are unavailable during testing, we propose an approach based on cross-modal knowledge distillation with a teacher-student model structure. The teacher model is trained with multiple data modalities, while the student model is designed to operate effectively with reduced modalities. In experiments on textitconnected autonomous driving with ground vehicles and textitaerial-ground vehicles collaboration, our method improves driving safety by up to it 20.7%, surpassing the best-existing baseline in detecting potential accidents and making safe driving decisions. More information can be found on our website https://ruiiu.github.io/mmcd.