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Showing posts from March, 2019

Missing Rare Events in Autonomous Vehicle Simulation

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Missing Rare Events in Simulation: A highly accurate simulation and system model doesn't solve the problem of what scenarios to simulate. If you don't know what edge cases to simulate, your system won't be safe. It is common, and generally desirable, to use vehicle-level simulation rather than on-road operation is used as a proxy field testing strategy. Simulation offers a number of potential advantages over field testing of a real vehicle including lower marginal cost per mile, better scalability, and reduced risk to the public from testing. Ultimately, simulation is based upon data that generates scenarios used to exercise the system under test, commonly called the simulation workload. The validity of the simulation workload is just as relevant as the validity of the simulation models and software. Simulation-based validation is often accomplished with a weighting of scenarios that is intentionally different than the expected operational profile. Such an appr

Dealing with Edge Cases in Autonomous Vehicle Validation

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Dealing with Edge Cases: Some failures are neither random nor independent. Moreover, safety is typically more about dealing with unusual cases. This means that brute force testing is likely to miss important edge case safety issues. A significant limitation to a field testing argument is the assumption of random independent failures inherent in the statistical analysis. Arguing that software failures are random and independent is clearly questionable, since multiple instances of a system will have identical software defects.  Moreover, arguing that the arrival of exceptional external events is random and independent across a fleet is clearly incorrect in the general case. A few simple examples of correlated events between vehicles in a fleet include: ·         Timekeeping events (e.g. daylight savings time, leap second) ·         Extreme weather (e.g. tornado, tsunami, flooding, blizzard white-out, wildfires) affecting multiple systems in the same geographic area ·  

The Fly-Fix-Fly Antipattern for Autonomous Vehicle Validation

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Fly-Fix-Fly Antipattern: Brute force mileage accumulation to fix all the problems you see on the road won't get you all the way to being safe. For so very many reasons... This A400m crash was caused by corrupted software calibration parameters. In practice, autonomous vehicle field testing has typically not been a deployment of a finished product to demonstrate a suitable integrity level. Rather, it is an iterative development approach that can amount to debugging followed by attempting to achieve improved system dependability over time based on field experience. In the aerospace domain this is called a “fly-fix-fly” strategy. The system is operated and each problem that crops up in operations is fixed in an attempt to remove all defects. While this can help improve safety, it is most effective for a rigorously engineered system that is nearly perfect to begin with, and for which the fixes do not substantially alter the vehicle’s design in ways that could produce new safet

The Insufficient Testing Pitfall for autonomous system safety

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The Insufficient Testing Pitfall: Testing less than the target failure rate doesn't prove you are safe.  In fact you probably need to test for about 10x the target failure rate to be reasonably sure you've met it. For life critical systems this means too much testing to be feasible. In a field testing argumentation approach a fleet of systems is tested in real-world conditions to build up confidence. At a certain point the testers declare that the system has been demonstrated to be safe and proceed with production deployment. An appropriate argumentation approach for field testing is that a sufficiently large number of exposure hours have been attained in a highly representative real-world environment. In other words, this is a variant of a proven in use argument in which the “use” was via testing rather than a production deployment. As such, the same proven in use argumentation issues apply, including especially the needs for representativeness and statistical signifi

The Human Filter Pitfall for autonomous system safety

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The Human Filter Pitfall: Using data from human-driven vehicles has gaps corresponding to situations a human knows to avoid getting into in the first place, but which an autonomous system might experience. The operational history (and thus the failure history) of many systems is filtered by human control actions, preemptive incident avoidance actions and exposure to operator-specific errors. This history might not cover the entirety of the required functionality, might primarily cover the system in a comparatively low risk environment, and might under-represent failures that manifest infrequently with human operators present. From a proven in use perspective, trying to use data from human-operated vehicles, such as historical crash data, might be insufficient to establish safety requirements or a basis for autonomous vehicle safety metrics. The situations in which human-operated vehicles have trouble may not be the same situations that autonomous systems find difficult. It is