{"work":{"id":"f07d6be8-7c83-4e2c-9f71-013acac7ffda","openalex_id":null,"doi":null,"arxiv_id":"2406.08481","raw_key":null,"title":"Enhancing End-to-End Autonomous Driving with Latent World Model","authors":null,"authors_text":"Yingyan Li, Lue Fan, Jiawei He, Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang","year":2024,"venue":"cs.CV","abstract":"In autonomous driving, end-to-end planners directly utilize raw sensor data, enabling them to extract richer scene features and reduce information loss compared to traditional planners. This raises a crucial research question: how can we develop better scene feature representations to fully leverage sensor data in end-to-end driving? Self-supervised learning methods show great success in learning rich feature representations in NLP and computer vision. Inspired by this, we propose a novel self-supervised learning approach using the LAtent World model (LAW) for end-to-end driving. LAW predicts future scene features based on current features and ego trajectories. This self-supervised task can be seamlessly integrated into perception-free and perception-based frameworks, improving scene feature learning and optimizing trajectory prediction. LAW achieves state-of-the-art performance across multiple benchmarks, including real-world open-loop benchmark nuScenes, NAVSIM, and simulator-based closed-loop benchmark CARLA. The code is released at https://github.com/BraveGroup/LAW.","external_url":"https://arxiv.org/abs/2406.08481","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-22T07:41:15.097114+00:00","pith_arxiv_id":"2406.08481","created_at":"2026-05-08T16:58:32.804579+00:00","updated_at":"2026-05-22T07:41:15.097114+00:00","title_quality_ok":true,"display_title":"Enhancing End-to-End Autonomous Driving with Latent World Model","render_title":"Enhancing End-to-End Autonomous Driving with Latent World Model"},"hub":{"state":{"work_id":"f07d6be8-7c83-4e2c-9f71-013acac7ffda","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":22,"external_cited_by_count":null,"distinct_field_count":2,"first_pith_cited_at":"2025-10-14T17:59:47+00:00","last_pith_cited_at":"2026-05-21T13:20:51+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-25T20:15:57.759503+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":6},{"context_role":"baseline","n":4}],"polarity_counts":[{"context_polarity":"background","n":6},{"context_polarity":"baseline","n":4}],"runs":{},"summary":{},"graph":{},"authors":[]}}