In summary, this work develops a full-stack autonomous driving system comprising a smart labeling pipeline for dataset construction, a lightweight multi-task perception model, and a modular decision system. The edge deployment on the NVIDIA AGX platform demonstrates its practical viability for enhancing advanced driver-assistance systems (ADAS).

Our autonomous driving system upgrades ADAS capabilities via three core innovations: First, "Multi-task Intelligent Labeling" overcomes localized data mass-production bottlenecks. Second, "Multi-task Gradient Balancing Technology" eliminates cross-task conflicts to achieve SOTA performance with high cross-domain transfer potential. Third, "Modular Generative Path Planning" address the traditional E2E black-box limitations, drastically reduce real-world collision rates and displacement errors.

This full-stack autonomous driving system enhances ADAS via three innovations: 1) "Multi-task Intelligent Labeling" overcomes local data production bottlenecks. 2) "Gradient Balancing" resolves cross-task conflicts to achieve SOTA performance with high transfer potential. 3) "Modular Generative Path Planning" eliminates black-box flaws, drastically reducing real-world collisions and displacement errors. Together, they provide a highly safe, low-cost, premium self-driving solution.