用预训练模型加速自动驾驶场景验证，聚焦高难度场景提升测试效率。
Foundation Models for Rapid Autonomy Validation
- 用掩码自编码器学习驾驶场景表征，构建可分组的语义空间。
- 基于碰撞概率为场景打分，优先测试高风险类型，显著降低验证里程需求。
- 适合需要高效安全验证的自动驾驶研发团队使用。
我们针对自动驾驶性能验证难题提出解决方案。自动驾驶系统需在各种驾驶场景中测试，包括罕见事件，以确保安全性并避免边缘情况下的异常行为。目前企业依赖数百万英里的真实模拟行驶来暴露系统，但成本高昂。为此，我们引入行为基础模型——掩码自编码器（MAE），用于重建驾驶场景。该模型通过两种互补方式提升验证效率：(i) 利用学习到的嵌入空间对相似场景进行聚类；(ii) 微调模型以预测场景碰撞概率，进而标注场景难度。将难度评分作为重要性权重，用于加权采样不同类别的场景。该方法能更快速地估算碰撞率与严重程度，同时保证对所有类型场景的覆盖，显著提升验证效率。
原文摘要 · Abstract (English)
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To address scalability and coverage, we propose the use of a behavior foundation model, specifically a masked autoencoder (MAE), trained to reconstruct driving scenarios. We leverage the foundation model in two complementary ways: we (i) use the learned embedding space to group qualitatively similar scenarios together and (ii) fine-tune the model to label scenario difficulty based on the likelihood of a collision upon simulation. We use the difficulty scoring as importance weighting for the groups of scenarios. The result is an approach which can more rapidly estimate the rates and severity of collisions by prioritizing hard scenarios while ensuring exposure to every kind of driving scenario.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。