CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910144632193024 |
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| author | Liu, Pei Ning, Qingtian Lu, Xinyan Liu, Haipeng Ma, Weiliang She, Dangen Jia, Peng Lang, Xianpeng Ma, Jun |
| author_facet | Liu, Pei Ning, Qingtian Lu, Xinyan Liu, Haipeng Ma, Weiliang She, Dangen Jia, Peng Lang, Xianpeng Ma, Jun |
| contents | The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs): they lack cognitive inertia. Operating on isolated snapshots, these models cannot form a continuous understanding of the environment, leading to erratic decision jitter and a failure to execute complex, multi-step maneuvers. To remedy this, we introduce CogDriver, a framework designed to build a stable internal representation by instilling this crucial cognitive property. Our work makes two key contributions: (1) We present CogDriver-Data, a large-scale vision-language-action dataset whose narrative annotations provide the supervisory signal for learning temporal dynamics and persistent intent. (2) We develop the CogDriver-Agent, an architecture featuring a sparse temporal memory to maintain a stable internal state. This is enabled by a spatiotemporal knowledge distillation approach that explicitly teaches decision coherence. Comprehensive experiments validate our paradigm: CogDriver-Agent achieves a 22% increase in the closed-loop Driving Score on Bench2Drive and a 21% reduction in mean L2 error on nuScenes, establishing a new state-of-the-art. These significant gains in both long-term decision-making and imitation accuracy provide strong evidence that our agent successfully maintains a temporally coherent internal state, bridging the gap toward more reliable autonomous driving. Project link: https://ocean-luna.github.io/CogDriver.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving Liu, Pei Ning, Qingtian Lu, Xinyan Liu, Haipeng Ma, Weiliang She, Dangen Jia, Peng Lang, Xianpeng Ma, Jun Computer Vision and Pattern Recognition The pursuit of autonomous agents capable of temporally coherent planning is hindered by a fundamental flaw in current vision-language models (VLMs): they lack cognitive inertia. Operating on isolated snapshots, these models cannot form a continuous understanding of the environment, leading to erratic decision jitter and a failure to execute complex, multi-step maneuvers. To remedy this, we introduce CogDriver, a framework designed to build a stable internal representation by instilling this crucial cognitive property. Our work makes two key contributions: (1) We present CogDriver-Data, a large-scale vision-language-action dataset whose narrative annotations provide the supervisory signal for learning temporal dynamics and persistent intent. (2) We develop the CogDriver-Agent, an architecture featuring a sparse temporal memory to maintain a stable internal state. This is enabled by a spatiotemporal knowledge distillation approach that explicitly teaches decision coherence. Comprehensive experiments validate our paradigm: CogDriver-Agent achieves a 22% increase in the closed-loop Driving Score on Bench2Drive and a 21% reduction in mean L2 error on nuScenes, establishing a new state-of-the-art. These significant gains in both long-term decision-making and imitation accuracy provide strong evidence that our agent successfully maintains a temporally coherent internal state, bridging the gap toward more reliable autonomous driving. Project link: https://ocean-luna.github.io/CogDriver.github.io/. |
| title | CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.00789 |