Autonomous driving paper index
DeFT: Maintaining Determinism and Extracting Unit Tests for Autonomous Driving Planning
One-line summary
This repository corresponds to the ICSE 2026 Research Track paper and its accompanying artifact, DeFT, a tool and methodology designed to improve testing reliability in autonomous driving systems by addressing non-determinism in planning tests.
Engineering notes
Key topics: autonomous driving system, autonomous driving, planning. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This repository corresponds to the ICSE 2026 Research Track paper and its accompanying artifact, DeFT, a tool and methodology designed to improve testing reliability in autonomous driving systems by addressing non-determinism in planning tests. Traditional system-level scenario tests often produce varying outcomes, making failure reproduction and debugging challenging. DeFT is a methodology that converts non-deterministic system-level scenario tests into deterministic module-level tests by extracting and reconstructing module inputs.
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