{"total":95,"rows":[{"id":"2303.12077","title":"VAD: Vectorized Scene Representation for Efficient Autonomous Driving","created":"2023-03-21","cited_by_count":622,"area":"driving"},{"id":"2207.07601","title":"ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning","created":"2022-07-15","cited_by_count":368,"area":"driving"},{"id":"2402.13243","title":"VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning","created":"2024-02-20","cited_by_count":289,"area":"driving"},{"id":"2312.03031","title":"Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?","created":"2023-12-05","cited_by_count":242,"area":"driving"},{"id":"2405.19620","title":"SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation","created":"2024-05-30","cited_by_count":241,"area":"driving"},{"id":"2109.04456","title":"NEAT: Neural Attention Fields for End-to-End Autonomous Driving","created":"2021-09-09","cited_by_count":174,"area":"driving"},{"id":"2305.06242","title":"Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving","created":"2023-05-10","cited_by_count":169,"area":"driving"},{"id":"2307.01492","title":"FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation","created":"2023-07-04","cited_by_count":165,"area":"driving"},{"id":"2210.07729","title":"Model-Based Imitation Learning for Urban Driving","created":"2022-10-14","cited_by_count":150,"area":"driving"},{"id":"2308.00398","title":"DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving","created":"2023-08-01","cited_by_count":144,"area":"driving"},{"id":"2204.13683","title":"KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients","created":"2022-04-28","cited_by_count":96,"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":"2402.05746","title":"Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents","created":"2024-02-08","cited_by_count":91,"area":"driving"},{"id":"2302.00673","title":"ADAPT: Action-aware Driving Caption Transformer","created":"2023-02-01","cited_by_count":78,"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":"2312.16108","title":"LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving","created":"2023-12-26","cited_by_count":57,"area":"driving"},{"id":"2304.05277","title":"Graph-based Topology Reasoning for Driving Scenes","created":"2023-04-11","cited_by_count":54,"area":"driving"},{"id":"2404.06892","title":"SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving","created":"2024-04-10","cited_by_count":47,"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":"2503.12030","title":"Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training","created":"2025-03-15","cited_by_count":39,"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":"2104.07830","title":"Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles","created":"2021-04-16","cited_by_count":33,"area":"driving"},{"id":"2311.08100","title":"PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving","created":"2023-11-14","cited_by_count":33,"area":"driving"},{"id":"2308.01471","title":"Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving","created":"2023-08-02","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":"2402.19341","title":"RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving","created":"2024-02-29","cited_by_count":26,"area":"driving"},{"id":"2104.12861","title":"Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation","created":"2021-04-18","cited_by_count":25,"area":"driving"},{"id":"2303.01212","title":"Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review","created":"2023-03-02","cited_by_count":25,"area":"driving"},{"id":"2403.19098","title":"GraphAD: Interaction Scene Graph for End-to-end Autonomous Driving","created":"2024-03-28","cited_by_count":25,"area":"driving"},{"id":"2401.08045","title":"Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities","created":"2024-01-16","cited_by_count":24,"area":"driving"},{"id":"2106.05966","title":"Learning by Watching","created":"2021-06-10","cited_by_count":22,"area":"driving"},{"id":"2506.07497","title":"Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency","created":"2025-06-09","cited_by_count":22,"area":"driving"},{"id":"2404.09496","title":"Towards Collaborative Autonomous Driving: Simulation Platform and End-to-End System","created":"2024-04-15","cited_by_count":20,"area":"driving"},{"id":"2502.18042","title":"VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion","created":"2025-02-25","cited_by_count":20,"area":"driving"},{"id":"2312.12144","title":"M-BEV: Masked BEV Perception for Robust Autonomous Driving","created":"2023-12-19","cited_by_count":19,"area":"driving"},{"id":"2204.10320","title":"SelfD: Self-Learning Large-Scale Driving Policies From the Web","created":"2022-04-21","cited_by_count":18,"area":"driving"},{"id":"2104.08862","title":"End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving","created":"2021-04-18","cited_by_count":17,"area":"driving"},{"id":"2506.00034","title":"GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous Driving","created":"2025-05-27","cited_by_count":16,"area":"driving"},{"id":"2507.17596","title":"PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving","created":"2025-07-23","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":"2406.03474","title":"AD-H: Language-guided Autonomous Driving with Hierarchical Agents","created":"2024-06-05","cited_by_count":15,"area":"driving"},{"id":"2408.05075","title":"DeepInteraction++: Multi-Modality Interaction for Autonomous Driving","created":"2024-08-09","cited_by_count":15,"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":"2405.19687","title":"Autonomous Driving with Spiking Neural Networks","created":"2024-05-30","cited_by_count":13,"area":"driving"},{"id":"2207.07895","title":"JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes","created":"2022-07-16","cited_by_count":11,"area":"driving"},{"id":"2401.01065","title":"BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving","created":"2024-01-02","cited_by_count":11,"area":"driving"},{"id":"2404.01486","title":"QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving","created":"2024-04-01","cited_by_count":11,"area":"driving"},{"id":"2410.23780","title":"Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map","created":"2024-10-31","cited_by_count":11,"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":"2407.15843","title":"CarFormer: Self-Driving with Learned Object-Centric Representations","created":"2024-07-22","cited_by_count":8,"area":"driving"},{"id":"2504.12696","title":"Collaborative Perception Datasets for Autonomous Driving: A Review","created":"2025-04-17","cited_by_count":8,"area":"driving"},{"id":"2506.10145","title":"RoCA: Robust Cross-Domain End-to-End Autonomous Driving","created":"2025-06-11","cited_by_count":8,"area":"driving"},{"id":"2510.19578","title":"VGD: Visual Geometry Gaussian Splatting for Feed-Forward Surround-view Driving Reconstruction","created":"2025-10-22","cited_by_count":8,"area":"driving"},{"id":"2407.06546","title":"Exploring the Causality of End-to-End Autonomous Driving","created":"2024-07-09","cited_by_count":7,"area":"driving"},{"id":"2511.19914","title":"CoC-VLA: Delving into Adversarial Domain Transfer for Explainable Autonomous Driving via Chain-of-Causality Visual-Language-Action Model","created":"2025-11-25","cited_by_count":7,"area":"driving"},{"id":"2510.23205","title":"VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting","created":"2025-10-27","cited_by_count":6,"area":"driving"},{"id":"2511.07106","title":"HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving","created":"2025-11-10","cited_by_count":6,"area":"driving"},{"id":"2409.17659","title":"Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning","created":"2024-09-26","cited_by_count":5,"area":"driving"},{"id":"2502.07631","title":"Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving","created":"2025-02-11","cited_by_count":5,"area":"driving"},{"id":"2512.06865","title":"Spatial Retrieval Augmented Autonomous Driving","created":"2025-12-07","cited_by_count":5,"area":"driving"},{"id":"2404.12867","title":"FipTR: A Simple yet Effective Transformer Framework for Future Instance Prediction in Autonomous Driving","created":"2024-04-19","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":"2409.09777","title":"EgoFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving","created":"2024-09-15","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":"2510.24052","title":"SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration","created":"2025-10-28","cited_by_count":3,"area":"driving"},{"id":"2511.01768","title":"UniLION: Towards Unified Autonomous Driving Model with Linear Group RNNs","created":"2025-11-03","cited_by_count":3,"area":"driving"},{"id":"2601.10512","title":"SatMap: Revisiting Satellite Maps as Prior for Online HD Map Construction","created":"2026-01-15","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":"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":"2509.19973","title":"OmniScene: Attention-Augmented Multimodal 4D Scene Understanding for Autonomous Driving","created":"2025-09-24","cited_by_count":2,"area":"driving"},{"id":"2509.23641","title":"From Static to Dynamic: a Survey of Topology-Aware Perception in Autonomous Driving","created":"2025-09-28","cited_by_count":2,"area":"driving"},{"id":"2510.24734","title":"DrivingScene: A Multi-Task Online Feed-Forward 3D Gaussian Splatting Method for Dynamic Driving Scenes","created":"2025-10-14","cited_by_count":2,"area":"driving"},{"id":"2512.01882","title":"New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehicles","created":"2025-12-01","cited_by_count":2,"area":"driving"},{"id":"2112.00219","title":"Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles","created":"2021-12-01","cited_by_count":1,"area":"driving"},{"id":"2205.05551","title":"NMR: Neural Manifold Representation for Autonomous Driving","created":"2022-05-11","cited_by_count":1,"area":"driving"},{"id":"2210.06758","title":"Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving","created":"2022-10-13","cited_by_count":1,"area":"driving"},{"id":"2309.09756","title":"Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving","created":"2023-09-18","cited_by_count":1,"area":"driving"},{"id":"2508.06074","title":"ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception","created":"2025-08-08","cited_by_count":1,"area":"driving"},{"id":"2602.23499","title":"TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving","created":"2026-02-26","cited_by_count":1,"area":"driving"},{"id":"2604.03497","title":"Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving","created":"2026-04-03","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":"2607.19774","title":"Defer to Plan: Adaptive Multi-Agent Fusion for End-to-End V2X Driving","created":"2026-07-22","cited_by_count":1,"area":"driving"},{"id":"2310.13077","title":"NeuroSMPC: A Neural Network guided Sampling Based MPC for On-Road Autonomous Driving","created":"2023-10-19","cited_by_count":0,"area":"driving"},{"id":"2503.11062","title":"Active Learning from Scene Embeddings for End-to-End Autonomous Driving","created":"2025-03-14","cited_by_count":0,"area":"driving"},{"id":"2505.13817","title":"InstanceBEV: Unifying Instance and BEV Representation for 3D Panoptic Segmentation","created":"2025-05-20","cited_by_count":0,"area":"driving"},{"id":"2508.20762","title":"SKGE-SWIN: End-To-End Autonomous Vehicle Waypoint Prediction and Navigation Using Skip Stage Swin Transformer","created":"2025-08-28","cited_by_count":0,"area":"driving"},{"id":"2511.19011","title":"End-to-end Autonomous Vehicle Following System using Monocular Fisheye Camera","created":"2025-11-24","cited_by_count":0,"area":"driving"},{"id":"2603.17714","title":"From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving","created":"2026-03-18","cited_by_count":0,"area":"driving"},{"id":"2604.16436","title":"Fuzzy Encoding-Decoding to Improve Spiking Q-Learning Performance in Autonomous Driving","created":"2026-04-06","cited_by_count":0,"area":"driving"},{"id":"2604.28111","title":"GSDrive: Reinforcing Driving Policies by Multi-mode Future Trajectory Probing with 3D Gaussian Splatting Environment","created":"2026-04-30","cited_by_count":0,"area":"driving"},{"id":"2606.16354","title":"GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving","created":"2026-06-15","cited_by_count":0,"area":"driving"},{"id":"2607.04689","title":"A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving","created":"2026-07-06","cited_by_count":0,"area":"driving"},{"id":"2607.09138","title":"BeyondSight: Object Permanence for End-to-End Autonomous Driving","created":"2026-07-10","cited_by_count":0,"area":"driving"},{"id":"2608.29005","title":"A Degradation-Tolerance Benchmark for Camera-Only End-to-End Driving","created":"2026-08-29","cited_by_count":0,"area":"driving"},{"id":"2609.10377","title":"Data-Driven Risk Fields for Safer End-to-End Autonomous Driving","created":"2026-09-09","cited_by_count":0,"area":"driving"}],"next_cursor":null}