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<div class="section abstract"><div class="htmlview paragraph">The performance of environment perceiving sensors such as e.g. lidar, radar, camera and ultrasonic sensors is safety critical for automated driving vehicles. Therefore, one has to assess the sensors’ performance to assure the automated driving system’s safety. The performance of these sensors is however to some degree sensitive towards adverse weather conditions. A challenge is to quantify the effect of adverse weather conditions on the sensor’s performance early in the development of an automated driving system. This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance. To this end, we combine basic probabilistic models for key rainfall parameters with Mie theory and the theory of signal detection in a Monte Carlo simulation framework. This allows to analyze and optimize a sensor’s design early in the sensor development, when physical testing is not yet possible. A challenge not addressed in this work is to include the effect of road spray water on the lidar’s performance. Combining the effect of other noise sources with the presented framework in a ray tracer is an opportunity for realistic physical lidar simulations and would allow to virtually estimate the performance of a lidar’s object detection and tracking performance. Such simulations could contribute to verify the safety of automated driving functionalities.</div></div>

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The design storm approach with event‐based rainfall‐runoff models is a standard method for design flood estimation in ungauged catchments. The approach is conceptually simple and computationally inexpensive, but the underlying assumptions can lead to flawed design flood estimations. In particular, the implied average recurrence interval (ARI) neutrality between rainfall and runoff neglects uncertainty in other important parameters, leading to an underestimation of design floods. The selection of a single representative critical rainfall duration in the analysis leads to an additional underestimation of design floods. One way to overcome these nonconservative approximations is the use of a continuous rainfall‐runoff model, which is associated with significant computational cost and requires rainfall input data that are often not readily available. As an alternative, we propose a novel Probabilistic Design Storm method that combines event‐based flood modeling with basic probabilistic models and concepts from reliability analysis, in particular the First‐Order Reliability Method (FORM). The proposed methodology overcomes the limitations of the standard design storm approach, while utilizing the same input information and models without excessive computational effort. Additionally, the Probabilistic Design Storm method allows deriving so‐called design charts, which summarize representative design storm events (combinations of rainfall intensity and other relevant parameters) for floods with different return periods. These can be used to study the relationship between rainfall and runoff return periods. We demonstrate, investigate, and validate the method by means of an example catchment located in the Bavarian Pre‐Alps, in combination with a simple hydrological model commonly used in practice.