{"ID":5937021,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T14:49:40.386444797Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05180","arxiv_id":"2607.05180","title":"VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving","abstract":"Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the vehicle can perceive and what it can physically do. Human drivers cope by anticipation, reasoning about the scene and re-budgeting speed, following distance, and steering before grip or sight is lost, whereas current autonomous driving systems at best react after the fact. This paper proposes VLM-CASE, a framework that gives an autonomous vehicle this anticipatory capacity while keeping its motion bounded by a formal safety model at all times. A vision-language model (VLM), fine-tuned with low-rank adaptation (LoRA), reasons about the scene from the front-camera image and reports the road surface and visibility conditions. This output parametrizes a context-adaptive safety envelope (CASE), derived from physical limits and the guarantees of responsibility-sensitive safety, that couples braking and steering through a shared friction budget. A model predictive controller then drives freely within the envelope, while the VLM runs asynchronously so it never blocks the real-time control loop. We validate the framework in closed-loop CARLA simulation on tasks that demand both lateral and longitudinal control, across a range of weather, road-surface, and lighting conditions. The resulting controller, VLM-CASE-MPC, completes all trials, outperforming a conventional MPC baseline and a state-of-the-art VLM-integrated controller. Ablations confirm that the gains come from context adaptation, with the friction and visibility adaptations proving complementary. Furthermore, the framework is controller-agnostic and pairs with almost any low-level controller, offering a promising direction for safe autonomous driving. The dataset and supplementary materials for VLM-CASE are available at https://github.com/ytj254/VLM-CASE.","short_abstract":"Adverse driving conditions, such as bad weather, remain a principal barrier to autonomous driving because they degrade two things at once: what the vehicle can perceive and what it can physically do. Human drivers cope by anticipation, reasoning about the scene and re-budgeting speed, following distance, and steering b...","url_abs":"https://arxiv.org/abs/2607.05180","url_pdf":"https://arxiv.org/pdf/2607.05180v1","authors":"[\"Tianjia Yang\",\"Ke Li\",\"Ruwen Qin\",\"Xianbiao Hu\"]","published":"2026-07-06T14:58:47Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"eess.SY\"]","methods":"[\"Language Model\",\"LoRA\"]","has_code":false,"code_links":[{"ID":613951,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T03:14:33.014478982Z","DeletedAt":null,"paper_id":5937021,"paper_url":"https://arxiv.org/abs/2607.05180","paper_title":"VLM-CASE: Vision-Language Model Enabled Context-Adaptive Safety Envelopes for Anticipatory Safe Autonomous Driving","repo_url":"https://github.com/ytj254/VLM-CASE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}