As a relatively nascent field, engineers developing autonomous vehicle (AV) technologies need frequent performance feedback on whether algorithms are performing the driving task competently. Further, because of the complexity of AV systems, it is often lower risk to frequently test small, incremental changes instead of delaying testing and accumulating a large number of changes to the algorithms. While simulation and closed course testing are useful and critically important tools, ultimately driving on public roads is necessary to truly understand system performance and identify potential edge cases. Maintaining a high safety standard to protect all road users during continual public road testing is of paramount importance for the AV industry.

The Waterfall methodology has a demonstrated track record for product safety, but does not provide much flexibility for prototyping and incremental testing. The Agile methodology is famous for enabling rapid development and incremental rollouts, but does not possess any inherent safety gates. When it comes to developing complex safety-critical autonomy features, particularly for dynamic environments such as in the case of autonomous vehicles, neither method is fitting.

This paper presents a hybrid methodology that strikes a balance between safe and rapid development of autonomy features for the AV industry.

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