Edge Case Research (United States)

The developed SPE clean-up procedure provides much better efficiency for a group of low molecular weight PAHs than the conventional procedure. It is therefore appropriate for extraction of gaseous PAHs from ambient air samples.

<div class="section abstract"><div class="htmlview paragraph">Validating the safety of Highly Automated Vehicles (HAVs) is a significant autonomy challenge. HAV safety validation strategies based solely on brute force on-road testing campaigns are unlikely to be viable. While simulations and exercising edge case scenarios can help reduce validation cost, those techniques alone are unlikely to provide a sufficient level of assurance for full-scale deployment without adopting a more nuanced view of validation data collection and safety analysis. Validation approaches can be improved by using higher fidelity testing to explicitly validate the assumptions and simplifications of lower fidelity testing rather than just obtaining sampled replication of lower fidelity results. Disentangling multiple testing goals can help by separating validation processes for requirements, environmental model sufficiency, autonomy correctness, autonomy robustness, and test scenario sufficiency. For autonomy approaches with implicit designs and requirements, such as machine learning training data sets, establishing observability points in the architecture can help ensure that vehicles pass the right tests for the right reason. These principles could improve both efficiency and effectiveness for demonstrating HAV safety as part of a phased validation plan that includes both a “driver test” and lifecycle monitoring as well as explicitly managing validation uncertainty.</div></div>

Ensuring the safety of fully autonomous vehicles requires a multi-disciplinary approach across all the levels of functional hierarchy, from hardware fault tolerance, to resilient machine learning, to cooperating with humans driving conventional vehicles, to validating systems for operation in highly unstructured environments, to appropriate regulatory approaches. Significant open technical challenges include validating inductive learning in the face of novel environmental inputs and achieving the very high levels of dependability required for full-scale fleet deployment. However, the biggest challenge may be in creating an end-to-end design and deployment process that integrates the safety concerns of a myriad of technical specialties into a unified approach.