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This paper discusses ongoing work in demonstrating research in mobileautonomy in challenging driving scenarios. In our approach, we addressfundamental technical issues to overcome critical barriers to assurance andregulation for large-scale deployments of autonomous systems. To this end, wepresent how we build robots that (1) can robustly sense and interpret theirenvironment using traditional as well as unconventional sensors; (2) can assesstheir own capabilities; and (3), vitally in the purpose of assurance and trust,can provide causal explanations of their interpretations and assessments. As itis essential that robots are safe and trusted, we design, develop, anddemonstrate fundamental technologies in real-world applications to overcomecritical barriers which impede the current deployment of robots in economicallyand socially important areas. Finally, we describe ongoing work in thecollection of an unusual, rare, and highly valuable dataset.

Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios