论文标题
Sense-Assess-解释（SAX）：在挑战现实世界驾驶方案中建立对自动驾驶汽车的信任
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios
论文作者
论文摘要
本文讨论了在挑战驾驶方案中展示移动自治方面研究的持续工作。在我们的方法中，我们解决了基本技术问题，以克服对自治系统大规模部署的保证和监管的关键障碍。为此，我们介绍了如何构建（1）可以使用传统和非常规的传感器来牢固地感知和解释其环境的机器人； （2）可以评估自己的能力； （3）在保证和信任的目的中，可以提供有关其解释和评估的因果解释。由于机器人至关重要，因此我们必须在现实世界中的应用中设计，开发和展示基本技术，以克服关键障碍，从而阻碍机器人在经济和社会上重要的领域中的当前部署。最后，我们描述了在收集不寻常，稀有且高度有价值的数据集中正在进行的工作。
This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.
