发表机构
Lomonosov Moscow State University; FusionBrain Lab; Moscow Institute of Physics and Technology; HSE University; Innopolis University; NUST MISIS（罗蒙诺索夫莫斯科国立大学; FusionBrain实验室; 莫斯科物理技术学院; 高等经济大学; 因诺波利斯大学; 国立研究型技术大学莫斯科国立钢铁合金学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有自动驾驶评估忽视交通规则合规性的问题，提出以交通标志为中心的基准TrafficSignBench，通过34种规则和5,800个场景评估规划器，并利用约束生成轨迹以提升合规性。
AI 中文摘要
自动驾驶规划器通常使用聚合指标（如驾驶得分、目的地到达率和碰撞率）进行评估，这些指标并未明确衡量对交通规则的遵守情况。因此，规划器可能取得较高的基准分数，同时仍表现出不安全或非法的行为，从而限制了其在现实世界部署中的适用性。为解决这一差距，我们引入了TrafficSignBench，一个大规模、以交通标志为中心的基准，用于对自动驾驶中的交通规则合规性进行系统且可解释的评估。我们的框架将基于真实地图的仿真（用于逼真的道路布局）与针对规则的程序化场景生成（用于对代表性不足的规则进行可扩展且均衡的覆盖）相结合。我们实现了对应于34种交通标志的交通规则，每种规则都配备了自动规则检查器，用于在闭环执行期间检测违规行为。这一设计产生了29,000个多样的道路场景和29种不同的测试场景类型，从而能够对特定规则的规划器行为进行受控评估。我们构建了5,800个测试场景，并证明当前的自动驾驶规划器尽管在标准评估指标上表现强劲，但可能表现出较差的交通规则合规性。为解决这一局限性，我们通过明确的交通标志约束将现有规划器转变为合规的轨迹专家，从而能够为微调生成高质量的神谕轨迹。
英文摘要
Autonomous driving planners are typically evaluated using aggregate metrics such as driving score, destination rate, and collision rate, which do not explicitly measure compliance with traffic rules. As a result, planners can achieve high benchmark scores while still exhibiting unsafe or illegal behaviors, limiting their applicability to real-world deployment. To address this gap, we introduce TrafficSignBench, a large-scale, traffic sign-centric benchmark for systematic and interpretable evaluation of traffic-rule compliance in autonomous driving. Our framework combines real-map-based simulation for realistic road layouts with rule-targeted procedural scenario generation for scalable and balanced coverage of underrepresented rules. We implement traffic rules corresponding to 34 traffic signs, each equipped with an automatic rule checker for detecting violations during closed-loop execution. This design yields 29,000 diverse road scenes and 29 distinct testing scenario types, enabling controlled evaluation of rule-specific planner behavior. We construct 5,800 testing scenes and demonstrate that current autonomous driving planners can exhibit poor traffic-rule compliance despite strong performance on standard evaluation metrics. To address this limitation, we transform existing planners into rule-compliant trajectory experts via explicit traffic-sign constraints, enabling scalable generation of high-quality oracle trajectories for fine-tuning.
