论文标题
使用可解释的传感器融合变压器安全增强的自动驾驶
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
论文作者
论文摘要
由于安全问题，自动驾驶汽车的大规模部署已不断延迟。一方面，全面的场景理解是必不可少的，缺乏这种理解会导致易受罕见但复杂的交通状况，例如突然出现未知物体。但是，从全球环境中进行推理需要访问多种类型的传感器以及多模式传感器信号的足够融合，这很难实现。另一方面，学习模型中缺乏可解释性也会因无法验证的故障原因阻碍安全性。在本文中，我们提出了一个安全增强的自主驾驶框架，称为可解释的传感器融合变压器（Interfuser），以完全处理和融合来自多模式多视图传感器的信息，以实现全面的场景理解和对抗性事件检测。此外，我们的框架是从我们的框架中产生的，该功能提供了更多的语义，并被利用以更好地约束操作以在安全集中。我们在Carla基准测试中进行了广泛的实验，在Carla基准测试中，我们的模型表现优于先前的方法，在公共卡拉排行榜上排名第一。我们的代码将在https://github.com/opendilab/interfuser上提供
Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of which would result in vulnerability to rare but complex traffic situations, such as the sudden emergence of unknown objects. However, reasoning from a global context requires access to sensors of multiple types and adequate fusion of multi-modal sensor signals, which is difficult to achieve. On the other hand, the lack of interpretability in learning models also hampers the safety with unverifiable failure causes. In this paper, we propose a safety-enhanced autonomous driving framework, named Interpretable Sensor Fusion Transformer(InterFuser), to fully process and fuse information from multi-modal multi-view sensors for achieving comprehensive scene understanding and adversarial event detection. Besides, intermediate interpretable features are generated from our framework, which provide more semantics and are exploited to better constrain actions to be within the safe sets. We conducted extensive experiments on CARLA benchmarks, where our model outperforms prior methods, ranking the first on the public CARLA Leaderboard. Our code will be made available at https://github.com/opendilab/InterFuser
