驾驶仿真论文趋势 2023–2026

主要结论

• 驾驶仿真论文从 2023 年的 969 篇增长到 2026 年的 2,200 篇，三年间扩大到约 2.3 倍。
• NeurIPS 是发驾驶仿真论文最多的会议，2023–2026 年共 2,111 篇，占四个会议总量的 36%。
• 交叉方向里涨得最快的是扩散模型：同时属于两个主题的论文从 2023 年的 27 篇增至 2026 年的 205 篇。
• 在「自动驾驶」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的运动规划是 2.4 倍，驾驶仿真是 2.3 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026$SE(2)$-Aware Conditional Distribution Transport for Vehicle Trajectory Generation
• ICML 2026A Penalty Approach For Differentiation Through Black-box Quadratic Programming Solvers
• ICLR 2026A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
• CVPR 2026A2GC Asymmetric Aggregation with Geometric Constraints for Locally Aggregated Descriptors
• NeurIPS 2025A compressive-expressive communication framework for compositional representations
• ICML 2025A Near-Optimal Single-Loop Stochastic Algorithm for Convex Finite-Sum Coupled Compositional Optimization
• ICLR 2025A Truncated Newton Method for Optimal Transport
• CVPR 20253D-SLNR: A Super Lightweight Neural Representation for Large-scale 3D Mapping
• NeurIPS 2024[Re] CUDA: Curriculum of Data Augmentation for Long‐tailed Recognition
• ICML 2024$H$-Consistency Guarantees for Regression
• ICLR 2024$\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation
• CVPR 2024Decentralized Directed Collaboration for Personalized Federated Learning
• NeurIPS 2023[Re] FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
• ICML 2023$H$-Consistency Bounds for Pairwise Misranking Loss Surrogates
• ICLR 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
• CVPR 20233DAvatarGAN: Bridging Domains for Personalized Editable Avatars
• NeurIPS 2026A Bitter Lesson for Data Filtering
• ICML 2026A Queueing-Theoretic Framework for Stability Analysis of LLM Inference with KV Cache Memory Constraints
• ICLR 2026Bayesian Neural Networks for Functional ANOVA Model
• CVPR 2026Active Perceptual Inference A Corticothalamic-Inspired Dynamic Nested Recurrent Network for

全部驾驶仿真论文

共 218 篇，第 2 / 5 页

51. ICML 2023Efficient Quantum Algorithms for Quantum Optimal Control
52. ICML 2023EM-Network: Oracle Guided Self-distillation for Sequence Learning
53. ICML 2023Enabling First-Order Gradient-Based Learning for Equilibrium Computation in Markets
54. ICML 2023Equivariant Architectures for Learning in Deep Weight Spaces
55. ICML 2023Equivariant Polynomials for Graph Neural Networks
56. ICML 2023Ewald-based Long-Range Message Passing for Molecular Graphs
57. ICML 2023Exphormer: Sparse Transformers for Graphs
58. ICML 2023Exponential Smoothing for Off-Policy Learning
59. ICML 2023Extrapolated Random Tree for Regression
60. ICML 2023Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization
61. ICML 2023Fast Algorithms for Distributed k-Clustering with Outliers
62. ICML 2023Fast Combinatorial Algorithms for Min Max Correlation Clustering
63. ICML 2023Fast Rates for Maximum Entropy Exploration
64. ICML 2023Federated Conformal Predictors for Distributed Uncertainty Quantification
65. ICML 2023Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
66. ICML 2023Fractional Denoising for 3D Molecular Pre-training
67. ICML 2023Future-conditioned Unsupervised Pretraining for Decision Transformer
68. ICML 2023Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
69. ICML 2023GC-Flow: A Graph-Based Flow Network for Effective Clustering
70. ICML 2023GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
71. ICML 2023Generalization Analysis for Contrastive Representation Learning
72. ICML 2023Generalized Teacher Forcing for Learning Chaotic Dynamics
73. ICML 2023Generating Language Corrections for Teaching Physical Control Tasks
74. ICML 2023GFlowNet-EM for Learning Compositional Latent Variable Models
75. ICML 2023GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
76. ICML 2023Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
77. ICML 2023Global optimality for Euclidean CCCP under Riemannian convexity
78. ICML 2023GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming
79. ICML 2023Gradient Descent Converges Linearly for Logistic Regression on Separable Data
80. ICML 2023Gradient-based Wang--Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space
81. ICML 2023Group Equivariant Fourier Neural Operators for Partial Differential Equations
82. ICML 2023H-Likelihood Approach to Deep Neural Networks with Temporal-Spatial Random Effects for High-Cardinality Categorical Features
83. ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
84. ICML 2023Hiding Data Helps: On the Benefits of Masking for Sparse Coding
85. ICML 2023High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
86. ICML 2023How Jellyfish Characterise Alternating Group Equivariant Neural Networks
87. ICML 2023How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
88. ICML 2023How to address monotonicity for model risk management?
89. ICML 2023Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
90. ICML 2023Identifying Useful Learnwares for Heterogeneous Label Spaces
91. ICML 2023Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression
92. ICML 2023Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback
93. ICML 2023Improved Algorithms for White-Box Adversarial Streams
94. ICML 2023Improved Policy Evaluation for Randomized Trials of Algorithmic Resource Allocation
95. ICML 2023Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs
96. ICML 2023Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
97. ICML 2023Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics
98. ICML 2023Interval Bound Interpolation for Few-shot Learning with Few Tasks
99. ICML 2023Is Learning Summary Statistics Necessary for Likelihood-free Inference?
100. ICML 2023Kernel Sufficient Dimension Reduction and Variable Selection for Compositional Data via Amalgamation

常见问题

2023–2026 年四大会议一共有多少篇驾驶仿真论文？

共 5,882 篇，按年份分别是 2023 年 969 篇、2024 年 1,308 篇、2025 年 1,405 篇、2026 年 2,200 篇。

NeurIPS 2026 有多少篇驾驶仿真论文？

NeurIPS 2026 有 940 篇驾驶仿真论文，上一届是 542 篇。

ICLR 2026 有多少篇驾驶仿真论文？

ICLR 2026 有 313 篇驾驶仿真论文，上一届是 228 篇。

ICML 2026 有多少篇驾驶仿真论文？

ICML 2026 有 363 篇驾驶仿真论文，上一届是 275 篇。

CVPR 2026 有多少篇驾驶仿真论文？

CVPR 2026 有 584 篇驾驶仿真论文，上一届是 360 篇。

哪个会议的驾驶仿真论文最多？

NeurIPS，2023–2026 年共 2,111 篇。