{"total":19,"rows":[{"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":"2602.18887","title":"SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World","created":"2026-02-21","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":"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":"2603.13842","title":"Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving","created":"2026-03-14","cited_by_count":4,"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":"2605.19524","title":"SafeAlign-VLA: A Negative-Enhanced Safe Alignment Framework for Risk-Aware Autonomous Driving","created":"2026-05-19","cited_by_count":2,"area":"driving"},{"id":"2607.08072","title":"Post-Training in End-to-End Autonomous Driving","created":"2026-07-09","cited_by_count":2,"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":"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":"2606.30537","title":"Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving","created":"2026-06-29","cited_by_count":1,"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":"2605.16737","title":"DriveSafer: End-to-End Autonomous Driving with Safety Guidance","created":"2026-05-16","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":"2609.02462","title":"VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation","created":"2026-09-02","cited_by_count":0,"area":"driving"},{"id":"2609.23488","title":"FeasibleFlow: One-Step Joint Transport of Configuration Feasibility and Trajectories for End-to-End Driving","created":"2026-09-20","cited_by_count":0,"area":"driving"},{"id":"2609.33190","title":"FocusDrive: Reasoning with Visual Focus for Autonomous Driving","created":"2026-09-27","cited_by_count":0,"area":"driving"},{"id":"2609.38862","title":"Efficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving","created":"2026-09-30","cited_by_count":0,"area":"driving"},{"id":"2610.00926","title":"A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform","created":"2026-10-01","cited_by_count":0,"area":"driving"}],"next_cursor":null}