{"total":125,"rows":[{"id":"2306.16927","title":"End-to-end Autonomous Driving: Challenges and Frontiers","created":"2023-06-29","cited_by_count":490,"area":"driving"},{"id":"2406.15349","title":"NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking","created":"2024-06-21","cited_by_count":354,"area":"driving"},{"id":"2406.03877","title":"Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving","created":"2024-06-06","cited_by_count":252,"area":"driving"},{"id":"2311.10813","title":"A Language Agent for Autonomous Driving","created":"2023-11-17","cited_by_count":164,"area":"driving"},{"id":"2506.04218","title":"Pseudo-Simulation for Autonomous Driving","created":"2025-06-04","cited_by_count":150,"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":"2311.01043","title":"LLM4Drive: A Survey of Large Language Models for Autonomous Driving","created":"2023-11-02","cited_by_count":136,"area":"driving"},{"id":"2403.04133","title":"Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving","created":"2024-03-07","cited_by_count":104,"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":"2403.04593","title":"Embodied Understanding of Driving Scenarios","created":"2024-03-07","cited_by_count":63,"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":"2310.05885","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":"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":"2509.17940","title":"DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving","created":"2025-09-22","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":"2503.08612","title":"HiP-AD: Hierarchical and Multi-Granularity Planning with Deformable Attention for Autonomous Driving in a Single Decoder","created":"2025-03-11","cited_by_count":35,"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":"2412.09602","title":"Hidden Biases of End-to-End Driving Datasets","created":"2024-12-12","cited_by_count":30,"area":"driving"},{"id":"2503.03074","title":"BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving","created":"2025-03-05","cited_by_count":26,"area":"driving"},{"id":"2309.10443","title":"Rethinking Imitation-based Planner for Autonomous Driving","created":"2023-09-19","cited_by_count":25,"area":"driving"},{"id":"2512.20563","title":"LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving","created":"2025-12-23","cited_by_count":23,"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":"2506.23982","title":"StyleDrive: Towards Driving-Style Aware Benchmarking of End-To-End Autonomous Driving","created":"2025-06-30","cited_by_count":21,"area":"driving"},{"id":"2506.09800","title":"Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving","created":"2025-06-11","cited_by_count":19,"area":"driving"},{"id":"2403.02877","title":"ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving","created":"2024-03-05","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":"2412.09647","title":"Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model","created":"2024-12-11","cited_by_count":14,"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":"2308.12779","title":"On Offline Evaluation of 3D Object Detection for Autonomous Driving","created":"2023-08-24","cited_by_count":11,"area":"driving"},{"id":"2401.08658","title":"End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023","created":"2023-12-26","cited_by_count":11,"area":"driving"},{"id":"2605.15120","title":"CLOVER: Closed-Loop Value Estimation and Ranking for End-to-End Autonomous Driving Planning","created":"2026-05-14","cited_by_count":11,"area":"driving"},{"id":"2508.21824","title":"DriveQA: Passing the Driving Knowledge Test","created":"2025-08-29","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":"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":"2606.19836","title":"World Engine: Towards the Era of Post-Training for Autonomous Driving","created":"2026-06-18","cited_by_count":8,"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":"2511.21584","title":"Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving","created":"2025-11-26","cited_by_count":7,"area":"driving"},{"id":"2606.07170","title":"Test-Time Trajectory Optimization for Autonomous Driving","created":"2026-06-05","cited_by_count":7,"area":"driving"},{"id":"2605.00066","title":"Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive","created":"2026-04-30","cited_by_count":6,"area":"driving"},{"id":"2512.06865","title":"Spatial Retrieval Augmented Autonomous Driving","created":"2025-12-07","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":"2403.11304","title":"Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving","created":"2024-03-17","cited_by_count":4,"area":"driving"},{"id":"2406.01544","title":"Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning","created":"2024-06-03","cited_by_count":4,"area":"driving"},{"id":"2410.09681","title":"LoRD: Adapting Differentiable Driving Policies to Distribution Shifts","created":"2024-10-13","cited_by_count":4,"area":"driving"},{"id":"2509.02754","title":"Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving","created":"2025-09-02","cited_by_count":4,"area":"driving"},{"id":"2509.17080","title":"CoPlanner: An Interactive Motion Planner with Contingency-Aware Diffusion for Autonomous Driving","created":"2025-09-21","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":"2604.03581","title":"HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving","created":"2026-04-04","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":"2503.08388","title":"V-Max: A Reinforcement Learning Framework for Autonomous Driving","created":"2025-03-11","cited_by_count":3,"area":"driving"},{"id":"2505.18945","title":"Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back","created":"2025-05-25","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":"2609.06055","title":"DriveZero: End-to-End Driving Beyond Human Demonstrations","created":"2026-09-05","cited_by_count":3,"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":"2603.15771","title":"CorrectionPlanner: Self-Correction Planner with Reinforcement Learning in Autonomous Driving","created":"2026-03-16","cited_by_count":2,"area":"driving"},{"id":"2603.25672","title":"Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving","created":"2026-03-26","cited_by_count":2,"area":"driving"},{"id":"2605.31116","title":"NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving","created":"2026-05-29","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"},{"id":"2406.08349","title":"Utilizing Navigation Paths to Generate Target Points for Enhanced End-to-End Autonomous Driving Planning","created":"2024-06-12","cited_by_count":1,"area":"driving"},{"id":"2409.02871","title":"Hybrid Imitation-Learning Motion Planner for Urban Driving","created":"2024-09-04","cited_by_count":1,"area":"driving"},{"id":"2411.07719","title":"EMPERROR: A Flexible Generative Perception Error Model for Probing Self-Driving Planners","created":"2024-11-12","cited_by_count":1,"area":"driving"},{"id":"2411.17253","title":"LHPF: Look back the History and Plan for the Future in Autonomous Driving","created":"2024-11-26","cited_by_count":1,"area":"driving"},{"id":"2502.11352","title":"A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems","created":"2025-02-17","cited_by_count":1,"area":"driving"},{"id":"2504.06584","title":"CAFE-AD: Cross-Scenario Adaptive Feature Enhancement for Trajectory Planning in Autonomous Driving","created":"2025-04-09","cited_by_count":1,"area":"driving"},{"id":"2512.03936","title":"Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response","created":"2025-12-03","cited_by_count":1,"area":"driving"},{"id":"2512.10660","title":"Closing the Navigation Compliance Gap in End-to-end Autonomous Driving","created":"2025-12-11","cited_by_count":1,"area":"driving"},{"id":"2601.01762","title":"AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving","created":"2026-01-05","cited_by_count":1,"area":"driving"},{"id":"2601.09377","title":"ReflexDiffusion: Reflection-Enhanced Trajectory Planning for High-lateral-acceleration Scenarios in Autonomous Driving","created":"2026-01-14","cited_by_count":1,"area":"driving"},{"id":"2602.03112","title":"A Unified Candidate Set with Scene-Adaptive Refinement via Diffusion for End-to-End Autonomous Driving","created":"2026-02-03","cited_by_count":1,"area":"driving"},{"id":"2602.07339","title":"RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving","created":"2026-02-07","cited_by_count":1,"area":"driving"},{"id":"2603.01023","title":"An Open-Source Modular Benchmark for Diffusion-Based Motion Planning in Closed-Loop Autonomous Driving","created":"2026-03-01","cited_by_count":1,"area":"driving"},{"id":"2604.05449","title":"Not All Agents Matter: From Global Attention Dilution to Risk-Prioritized Game Planning","created":"2026-04-07","cited_by_count":1,"area":"driving"},{"id":"2604.08366","title":"Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems","created":"2026-04-09","cited_by_count":1,"area":"driving"},{"id":"2605.13646","title":"Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling","created":"2026-05-13","cited_by_count":1,"area":"driving"},{"id":"2606.25509","title":"ASSCG: Just-Right Gating over Chattering for Fast-Slow LLM Planning in Autonomous Driving","created":"2026-06-24","cited_by_count":1,"area":"driving"},{"id":"2606.30537","title":"Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving","created":"2026-06-29","cited_by_count":1,"area":"driving"},{"id":"2607.13028","title":"TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale","created":"2026-07-14","cited_by_count":1,"area":"driving"},{"id":"2410.15607","title":"Reinforced Imitative Trajectory Planning for Urban Automated Driving","created":"2024-10-21","cited_by_count":0,"area":"driving"},{"id":"2411.00476","title":"PlanScope: Learning to Plan Within Decision Scope for Urban Autonomous Driving","created":"2024-11-01","cited_by_count":0,"area":"driving"},{"id":"2508.16947","title":"Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving","created":"2025-08-23","cited_by_count":0,"area":"driving"},{"id":"2509.13579","title":"TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning","created":"2025-09-16","cited_by_count":0,"area":"driving"},{"id":"2509.24313","title":"Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning","created":"2025-09-29","cited_by_count":0,"area":"driving"},{"id":"2510.07210","title":"HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving","created":"2025-10-08","cited_by_count":0,"area":"driving"},{"id":"2510.08571","title":"Scalable Offline Metrics for Autonomous Driving","created":"2025-10-09","cited_by_count":0,"area":"driving"},{"id":"2510.10503","title":"Align2Act: Instruction-Tuned Models for Human-Aligned Autonomous Driving","created":"2025-10-12","cited_by_count":0,"area":"driving"},{"id":"2512.13094","title":"Sequence of Expert: Boosting Imitation Planners for Autonomous Driving through Temporal Alternation","created":"2025-12-15","cited_by_count":0,"area":"driving"},{"id":"2602.08334","title":"Vec-QMDP: Vectorized POMDP Planning on CPUs for Real-Time Autonomous Driving","created":"2026-02-09","cited_by_count":0,"area":"driving"}],"next_cursor":"100"}