Autonomous Driving Assistance Systems (ADAS) rely on extensive testing to ensure safety and reliability, yet road scenario datasets often contain redundant cases that slow down the testing process without improving fault detection. To address this issue, we present a novel test prioritization framework that reduces redundancy while preserving geometric and behavioral diversity. Road scenarios are clustered based on geometric and dynamic features of the ADAS driving behavior, from which representative cases are selected to guarantee coverage. Roads are finally prioritized based on geometric complexity, driving difficulty, and historical failures, ensuring that the most critical and challenging tests are executed first. We evaluate our framework on the OPENCAT dataset and the Udacity self-driving car simulator using two ADAS models. On average, our approach achieves an 89% reduction in test suite size while retaining an average of 79% of failed road scenarios. The prioritization strategy improves early failure detection by up to 95x compared to random baselines.

翻译：自动驾驶辅助系统（ADAS）依赖大量测试以确保安全性与可靠性，然而道路场景数据集常包含冗余案例，这些案例不仅无助于提升故障检测能力，反而会拖慢测试进程。为解决这一问题，本文提出一种新颖的测试优先级排序框架，该框架能在保持几何与行为多样性的同时有效降低冗余。首先基于ADAS驾驶行为的几何特征与动态特征对道路场景进行聚类，从中选取代表性案例以保证覆盖度；随后依据几何复杂度、驾驶难度及历史故障记录对道路进行优先级排序，确保最关键且最具挑战性的测试得以优先执行。我们在OPENCAT数据集与Udacity自动驾驶汽车模拟器上使用两种ADAS模型对该框架进行评估。实验结果表明，本方法平均可缩减89%的测试集规模，同时保留平均79%的失败道路场景；相较于随机基线，优先级排序策略将早期故障检测效率最高提升至95倍。