SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring

arXiv:2608.25142 · cs.RO, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.RO, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Benchmark — 5× in this paper · explained in 2 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Decode — 2× in this paper · explained in 2 episodes
• Domain Shift — 2× in this paper · explained in 4 episodes
• Gaussian Splatting — 2× in this paper · explained in 4 episodes
• IEEE Intelligent Vehicles Symposium — 2× in this paper
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Average Displacement Error — 1× in this paper
• Benchmarking — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Final Displacement Error — 1× in this paper
• Flow Matching — 1× in this paper · explained in 8 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Latent Space — 1× in this paper · explained in 7 episodes
• Modularity — 1× in this paper · explained in 9 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Non-Compliant Trajectory — 1× in this paper
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
• UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
• RAP: 3D Rasterization Augmented End-to-End Planning
• FlowDrive: Energy Flow Field for End-to-End Autonomous Driving
• Driving on Registers
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• Driving Policy Transfer via Modularity and Abstraction
• Zero-Shot Cross-City Generalization in End-to-End Autonomous Driving: Self-Supervised versus Supervised Representations
• Scaling Self-Play for End-to-End Driving
• SimScale: Learning to Drive via Real-World Simulation at Scale
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
• GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
• Autonomous Driving in Reality with Reinforcement Learning and Image Translation
• SIND: A Drone Dataset at Signalized Intersection in China
• RoCA: Robust Cross-Domain End-to-End Autonomous Driving
• INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
• HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving

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• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving