Safety Assessment and Uncertainty Quantification of Automated Driver Assistance Systems Using Stochastic Analysis Methods
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- Safety Assessment and Uncertainty Quantification of Automated Driver Assistance Systems Using Stochastic Analysis Methods
This paper was produced for the 2019 NAFEMS World Congress in Quebec Canada
Advanced Driver Assistance Systems (ADAS) see a constantly growing attention by researchers and industries as more and more vehicles are equipped with such technology. It is generally expected to see first Automated Driving Systems (ADS) in the market in the next years. One of the most important aspects for reaching this goal and releasing ADS is testing and validation.
Several publications stated out, that the required mileage needed to proof the probability of failure of the system is impossible to reach in field operational tests. Therefore, statistical methods, e.g. Monte-Carlo simulation, combined with Software-in-the-Loop (SiL) simulation may help to overcome this limit. The field of reliability analysis, initially developed for structural mechanics, provides algorithms and approaches which can be applied to assess ADS. Due to the different kind of parameters and criteria, available methodologies need to be analyzed and adapted to ADS specific challenges.
In this paper, the presented process is based on event-based simulations where specific traffic scenarios are parametrized, simulated and analyzed by a set of criteria. By using predefined distribution functions for each input parameter a safety statement can be given by approximating the probability of failure for each traffic scenario by determining the unsafe region in the parameter space. Therefore, multiple steps of different algorithms are combined to ensure trustworthy results by being very efficient in reducing the number of required simulation runs.
|Date ||18th June 2019|
|Order Ref||NWC_19_191 Download|
|Non-member Price || £5.00 | $6.34 | €5.59 |
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