{"total":176,"rows":[{"id":"2306.16927","title":"End-to-end Autonomous Driving: Challenges and Frontiers","created":"2023-06-29","cited_by_count":490,"area":"driving"},{"id":"2303.05760","title":"GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving","created":"2023-03-10","cited_by_count":141,"area":"driving"},{"id":"2501.15564","title":"Diffusion-Based Planning for Autonomous Driving with Flexible Guidance","created":"2025-01-26","cited_by_count":141,"area":"driving"},{"id":"2212.11419","title":"Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios","created":"2022-12-21","cited_by_count":129,"area":"driving"},{"id":"2408.00415","title":"DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Driving","created":"2024-08-01","cited_by_count":98,"area":"driving"},{"id":"2503.12820","title":"Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation","created":"2025-03-17","cited_by_count":96,"area":"driving"},{"id":"2408.03601","title":"DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba","created":"2024-08-07","cited_by_count":87,"area":"driving"},{"id":"2404.14327","title":"PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving","created":"2024-04-22","cited_by_count":84,"area":"driving"},{"id":"2408.10845","title":"CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving","created":"2024-08-19","cited_by_count":77,"area":"driving"},{"id":"2308.01006","title":"FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving","created":"2023-08-02","cited_by_count":67,"area":"driving"},{"id":"2504.19580","title":"ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving","created":"2025-04-28","cited_by_count":67,"area":"driving"},{"id":"2404.07762","title":"NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving","created":"2024-04-11","cited_by_count":62,"area":"driving"},{"id":"2310.12432","title":"CAT: Closed-loop Adversarial Training for Safe End-to-End Driving","created":"2023-10-19","cited_by_count":54,"area":"driving"},{"id":"2402.05932","title":"Driving Everywhere with Large Language Model Policy Adaptation","created":"2024-02-08","cited_by_count":53,"area":"driving"},{"id":"2310.05885","title":"DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving","created":"2023-10-09","cited_by_count":46,"area":"driving"},{"id":"2408.01584","title":"GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS","created":"2024-08-02","cited_by_count":45,"area":"driving"},{"id":"2210.09539","title":"Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving","created":"2022-10-18","cited_by_count":44,"area":"driving"},{"id":"2601.05083","title":"Driving on Registers","created":"2026-01-08","cited_by_count":44,"area":"driving"},{"id":"2503.11650","title":"Centaur: Robust End-to-End Autonomous Driving with Test-Time Training","created":"2025-03-14","cited_by_count":42,"area":"driving"},{"id":"2505.15111","title":"iPad: Iterative Proposal-centric End-to-End Autonomous Driving","created":"2025-05-21","cited_by_count":42,"area":"driving"},{"id":"2510.04333","title":"RAP: 3D Rasterization Augmented End-to-End Planning","created":"2025-10-05","cited_by_count":41,"area":"driving"},{"id":"2503.12030","title":"Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training","created":"2025-03-15","cited_by_count":39,"area":"driving"},{"id":"2406.14556","title":"Asynchronous Large Language Model Enhanced Planner for Autonomous Driving","created":"2024-06-20","cited_by_count":38,"area":"driving"},{"id":"2212.06437","title":"DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles","created":"2022-12-13","cited_by_count":37,"area":"driving"},{"id":"2502.19908","title":"CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving","created":"2025-02-27","cited_by_count":34,"area":"driving"},{"id":"2408.09675","title":"Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey","created":"2024-08-19","cited_by_count":32,"area":"driving"},{"id":"2312.06352","title":"NuScenes-MQA: Integrated Evaluation of Captions and QA for Autonomous Driving Datasets using Markup Annotations","created":"2023-12-11","cited_by_count":31,"area":"driving"},{"id":"2412.09602","title":"Hidden Biases of End-to-End Driving Datasets","created":"2024-12-12","cited_by_count":30,"area":"driving"},{"id":"2601.22032","title":"Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving","created":"2026-01-29","cited_by_count":30,"area":"driving"},{"id":"2306.15713","title":"Rethinking Closed-loop Training for Autonomous Driving","created":"2023-06-27","cited_by_count":29,"area":"driving"},{"id":"2312.04316","title":"Towards Knowledge-driven Autonomous Driving","created":"2023-12-07","cited_by_count":29,"area":"driving"},{"id":"2406.17680","title":"End-to-End Autonomous Driving without Costly Modularization and 3D Manual Annotation","created":"2024-06-25","cited_by_count":28,"area":"driving"},{"id":"2403.19918","title":"CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning","created":"2024-03-29","cited_by_count":27,"area":"driving"},{"id":"2309.10443","title":"Rethinking Imitation-based Planner for Autonomous Driving","created":"2023-09-19","cited_by_count":25,"area":"driving"},{"id":"2503.06744","title":"CoDa-4DGS: Dynamic Gaussian Splatting with Context and Deformation Awareness for Autonomous Driving","created":"2025-03-09","cited_by_count":25,"area":"driving"},{"id":"2410.15774","title":"Generalizing Motion Planners with Mixture of Experts for Autonomous Driving","created":"2024-10-21","cited_by_count":22,"area":"driving"},{"id":"2206.03004","title":"Driving in Real Life with Inverse Reinforcement Learning","created":"2022-06-07","cited_by_count":18,"area":"driving"},{"id":"2401.12888","title":"Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies","created":"2024-01-23","cited_by_count":17,"area":"driving"},{"id":"2403.19648","title":"Human-compatible driving partners through data-regularized self-play reinforcement learning","created":"2024-03-28","cited_by_count":17,"area":"driving"},{"id":"2604.08535","title":"Fail2Drive: Benchmarking Closed-Loop Driving Generalization","created":"2026-04-09","cited_by_count":17,"area":"driving"},{"id":"2505.15880","title":"Challenger: Affordable Adversarial Driving Video Generation","created":"2025-05-21","cited_by_count":16,"area":"driving"},{"id":"2510.24108","title":"Zero-Human Demonstration End-to-end Autonomous Driving with Trajectory Scorer","created":"2025-10-28","cited_by_count":16,"area":"driving"},{"id":"2504.04338","title":"Data Scaling Laws for End-to-End Autonomous Driving","created":"2025-04-06","cited_by_count":15,"area":"driving"},{"id":"2603.06049","title":"Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models","created":"2026-03-06","cited_by_count":15,"area":"driving"},{"id":"2309.13614","title":"Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills","created":"2023-09-24","cited_by_count":14,"area":"driving"},{"id":"2409.09575","title":"Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model","created":"2024-09-15","cited_by_count":14,"area":"driving"},{"id":"2508.05402","title":"DistillDrive: End-to-End Multi-Mode Autonomous Driving Distillation by Isomorphic Hetero-Source Planning Model","created":"2025-08-07","cited_by_count":14,"area":"driving"},{"id":"2310.18301","title":"Interactive Joint Planning for Autonomous Vehicles","created":"2023-10-27","cited_by_count":13,"area":"driving"},{"id":"2602.20060","title":"MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Driving","created":"2026-02-23","cited_by_count":13,"area":"driving"},{"id":"2305.02649","title":"CCIL: Context-conditioned imitation learning for urban driving","created":"2023-05-04","cited_by_count":12,"area":"driving"},{"id":"2412.04209","title":"CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model","created":"2024-12-05","cited_by_count":12,"area":"driving"},{"id":"2207.12380","title":"Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehicles","created":"2022-07-25","cited_by_count":11,"area":"driving"},{"id":"2308.12779","title":"On Offline Evaluation of 3D Object Detection for Autonomous Driving","created":"2023-08-24","cited_by_count":11,"area":"driving"},{"id":"2403.13208","title":"CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories","created":"2024-03-19","cited_by_count":11,"area":"driving"},{"id":"2409.10320","title":"SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation","created":"2024-09-16","cited_by_count":10,"area":"driving"},{"id":"2410.15281","title":"LLM4AD: Large Language Models for Autonomous Driving -- Concept, Review, Benchmark, Experiments, and Future Trends","created":"2024-10-20","cited_by_count":10,"area":"driving"},{"id":"2412.02689","title":"Data Scaling Laws for Imitation Learning-Based End-to-End Autonomous Driving","created":"2024-12-03","cited_by_count":10,"area":"driving"},{"id":"2512.09864","title":"UniUGP: Unifying Understanding, Generation, and Planing For End-to-end Autonomous Driving","created":"2025-12-10","cited_by_count":10,"area":"driving"},{"id":"2503.03957","title":"Enhancing Autonomous Driving Safety with Collision Scenario Integration","created":"2025-03-05","cited_by_count":9,"area":"driving"},{"id":"2505.19516","title":"DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy","created":"2025-05-26","cited_by_count":9,"area":"driving"},{"id":"2507.21610","title":"Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition","created":"2025-07-29","cited_by_count":9,"area":"driving"},{"id":"2604.10856","title":"BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving","created":"2026-04-12","cited_by_count":9,"area":"driving"},{"id":"2605.31572","title":"nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving","created":"2026-05-29","cited_by_count":9,"area":"driving"},{"id":"2309.16397","title":"Uncertainty-Aware Decision Transformer for Stochastic Driving Environments","created":"2023-09-28","cited_by_count":8,"area":"driving"},{"id":"2511.07292","title":"PlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving","created":"2025-11-10","cited_by_count":8,"area":"driving"},{"id":"2605.19771","title":"Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives","created":"2026-05-19","cited_by_count":8,"area":"driving"},{"id":"2212.03323","title":"Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles","created":"2022-12-06","cited_by_count":7,"area":"driving"},{"id":"2311.18636","title":"End-to-end Autonomous Driving using Deep Learning: A Systematic Review","created":"2023-08-27","cited_by_count":7,"area":"driving"},{"id":"2505.17209","title":"LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios","created":"2025-05-22","cited_by_count":7,"area":"driving"},{"id":"2602.15891","title":"Learning to Drive in New Cities Without Human Demonstrations","created":"2026-02-09","cited_by_count":7,"area":"driving"},{"id":"2512.01993","title":"RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policies","created":"2025-12-01","cited_by_count":6,"area":"driving"},{"id":"2511.08214","title":"Prioritizing Perception-Guided Self-Supervision: A New Paradigm for Causal Modeling in End-to-End Autonomous Driving","created":"2025-11-11","cited_by_count":5,"area":"driving"},{"id":"2603.02413","title":"TruckDrive: Long-Range Autonomous Highway Driving Dataset","created":"2026-03-02","cited_by_count":5,"area":"driving"},{"id":"2605.10034","title":"Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving","created":"2026-05-11","cited_by_count":5,"area":"driving"},{"id":"2605.10904","title":"MDrive: Benchmarking Closed-Loop Cooperative Driving for End-to-End Multi-agent Systems","created":"2026-05-11","cited_by_count":5,"area":"driving"},{"id":"2503.15672","title":"GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving","created":"2025-03-19","cited_by_count":4,"area":"driving"},{"id":"2504.13785","title":"Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback","created":"2025-04-18","cited_by_count":4,"area":"driving"},{"id":"2510.15505","title":"Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving","created":"2025-10-17","cited_by_count":4,"area":"driving"},{"id":"2511.10403","title":"nuPlan-R: A Closed-Loop Planning Benchmark for Autonomous Driving via Reactive Multi-Agent Simulation","created":"2025-11-13","cited_by_count":4,"area":"driving"},{"id":"2511.13306","title":"DAP: A Discrete-token Autoregressive Planner for Autonomous Driving","created":"2025-11-17","cited_by_count":4,"area":"driving"},{"id":"2606.19641","title":"Scaling Self-Play for End-to-End Driving","created":"2026-06-17","cited_by_count":4,"area":"driving"},{"id":"2402.04720","title":"Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles","created":"2024-02-07","cited_by_count":3,"area":"driving"},{"id":"2409.14196","title":"Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review","created":"2024-09-21","cited_by_count":3,"area":"driving"},{"id":"2411.04653","title":"IGDrivSim: A Benchmark for the Imitation Gap in Autonomous Driving","created":"2024-11-07","cited_by_count":3,"area":"driving"},{"id":"2503.08388","title":"V-Max: A Reinforcement Learning Framework for Autonomous Driving","created":"2025-03-11","cited_by_count":3,"area":"driving"},{"id":"2509.10570","title":"Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey","created":"2025-09-11","cited_by_count":3,"area":"driving"},{"id":"2512.00021","title":"Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges","created":"2025-10-31","cited_by_count":3,"area":"driving"},{"id":"2603.28522","title":"RAD-LAD: Rule and Language Grounded Autonomous Driving in Real-Time","created":"2026-03-30","cited_by_count":3,"area":"driving"},{"id":"2605.18059","title":"Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations","created":"2026-05-18","cited_by_count":3,"area":"driving"},{"id":"2605.21139","title":"Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving","created":"2026-05-20","cited_by_count":3,"area":"driving"},{"id":"2606.17386","title":"TerraTransfer: Learning End-to-End Driving Policies Without Expert Demonstrations","created":"2026-06-16","cited_by_count":3,"area":"driving"},{"id":"2609.06055","title":"DriveZero: End-to-End Driving Beyond Human Demonstrations","created":"2026-09-05","cited_by_count":3,"area":"driving"},{"id":"2311.02850","title":"IR-STP: Enhancing Autonomous Driving with Interaction Reasoning in Spatio-Temporal Planning","created":"2023-11-06","cited_by_count":2,"area":"driving"},{"id":"2407.02508","title":"Physics-informed Imitative Reinforcement Learning for Real-world Driving","created":"2024-06-18","cited_by_count":2,"area":"driving"},{"id":"2504.01409","title":"Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios","created":"2025-04-02","cited_by_count":2,"area":"driving"},{"id":"2508.01778","title":"DiffSemanticFusion: Semantic Raster BEV Fusion for Autonomous Driving via Online HD Map Diffusion","created":"2025-08-03","cited_by_count":2,"area":"driving"},{"id":"2603.04071","title":"SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling","created":"2026-03-04","cited_by_count":2,"area":"driving"},{"id":"2603.15771","title":"CorrectionPlanner: Self-Correction Planner with Reinforcement Learning in Autonomous Driving","created":"2026-03-16","cited_by_count":2,"area":"driving"},{"id":"2606.08525","title":"DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving","created":"2026-06-07","cited_by_count":2,"area":"driving"},{"id":"2606.11019","title":"Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving","created":"2026-06-09","cited_by_count":2,"area":"driving"}],"next_cursor":"100"}