{"total":19,"rows":[{"id":"2306.16927","title":"End-to-end Autonomous Driving: Challenges and Frontiers","created":"2023-06-29","cited_by_count":490,"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":"2307.04370","title":"Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey","created":"2023-07-10","cited_by_count":103,"area":"driving"},{"id":"2305.10507","title":"ReasonNet: End-to-End Driving with Temporal and Global Reasoning","created":"2023-05-17","cited_by_count":93,"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":"2403.04593","title":"Embodied Understanding of Driving Scenarios","created":"2024-03-07","cited_by_count":63,"area":"driving"},{"id":"2310.17642","title":"Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models","created":"2023-10-26","cited_by_count":59,"area":"driving"},{"id":"2309.05186","title":"HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving","created":"2023-09-11","cited_by_count":50,"area":"driving"},{"id":"2312.04316","title":"Towards Knowledge-driven Autonomous Driving","created":"2023-12-07","cited_by_count":29,"area":"driving"},{"id":"2103.01882","title":"Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization","created":"2021-03-02","cited_by_count":22,"area":"driving"},{"id":"2307.10408","title":"Explaining Autonomous Driving Actions with Visual Question Answering","created":"2023-07-19","cited_by_count":16,"area":"driving"},{"id":"2505.18334","title":"CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning","created":"2025-05-23","cited_by_count":7,"area":"driving"},{"id":"2509.15968","title":"CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine","created":"2025-09-19","cited_by_count":7,"area":"driving"},{"id":"2601.10707","title":"See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection","created":"2026-01-15","cited_by_count":4,"area":"driving"},{"id":"2410.10577","title":"Words to Wheels: Vision-Based Autonomous Driving Understanding Human Language Instructions Using Foundation Models","created":"2024-10-14","cited_by_count":2,"area":"driving"},{"id":"2111.06017","title":"Yaw-Guided Imitation Learning for Autonomous Driving in Urban Environments","created":"2021-11-11","cited_by_count":1,"area":"driving"},{"id":"2305.10443","title":"SuperDriverAI: Towards Design and Implementation for End-to-End Learning-based Autonomous Driving","created":"2023-05-14","cited_by_count":1,"area":"driving"},{"id":"2603.05936","title":"OD-RASE: Ontology-Driven Risk Assessment and Safety Enhancement for Autonomous Driving","created":"2026-03-06","cited_by_count":1,"area":"driving"},{"id":"2609.38028","title":"doPlan: A Variable-Horizon Dataset for Multi-Stage Language-Conditioned Planning in Autonomous Driving","created":"2026-09-29","cited_by_count":0,"area":"driving"}],"next_cursor":null}