DeepNNCar is a low-cost research testbed for designing, training and testing autonomous driving pipelines and assurance components
System-level Assurance Case has become a critical requirement of the regulatory acceptance. However, problems such as human relaiance, lack of automation and design procedure makes it challenging to design a robust assurance case.
Though machine learning components have shown remarkable performance for challenging tasks such as autonomous driving ([NVIDIA DAVE-II](https://www.youtube.com/watch?v=NJU9ULQUwng&ab_channel=IProgrammerTV)), they have shown to be susceptible to slight shifts in the operating contexts, popularly known as out-of-distribution (OOD) data.
Cyber-Physical Systems need a contingency plan once it encounters high risk because of operational hazards.