Assessing the Impact of Fleet Size on Crowdsourced Mapping Using a Dissimilarity Measure

arXiv:2609.29198 · cs.RO, physics.data-an · Submitted 2026-09-24 · Read on arXiv

cs.RO, physics.data-an

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Clustering — 30× in this paper · explained in 2 episodes
• Aggregation — 13× in this paper · explained in 1 episode
• Accuracy — 11× in this paper · explained in 1 episode
• False Negatives — 9× in this paper
• False Positives — 9× in this paper
• Autonomous Driving — 5× in this paper
• High Definition — 4× in this paper
• F1 Score — 2× in this paper · explained in 3 episodes
• IEEE Intelligent Vehicles Symposium — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Advanced Driver Assistance Systems — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• High Definition Map Mapping and Update: A General Overview and Future Directions

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• 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