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• Existing perception and calibration methods for autonomous vehicles assume static baselines or rely on high-parallax and texture-rich scenes, limiting their reliability for articulated trucks.limitation 90%
• dCAP continuously estimates the 6-DoF relative pose between tractor and trailer cameras using a vision-based framework.method 95%
• dCAP employs a transformer with cross-view and temporal attention to robustly aggregate spatial cues while maintaining temporal consistency.technical 90%
• Integrated with BEVFormer, dCAP improves 3D object detection by replacing static calibration with dynamically predicted extrinsics.result 85%
• The STT4AT benchmark simulates semi-trailer trucks with synchronized multi-sensor suites and time-varying inter-rig geometry across diverse environments.method 90%
• Experiments demonstrate that dCAP achieves stable, accurate perception while addressing the limitations of static calibration in autonomous trucking.result 85%
• The dataset, development kit, and source code will be publicly released.technical 95%
• dCAP enables accurate perception under rapid articulation and occlusion conditions.technical 80%

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