Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.
Perception sensor modeling is essential for the safety validation of automated driving systems in virtual environments. Nevertheless, the community lacks a methodical approach to derive requirements for such sensor models that enables a serious application for safety validation in the first place. This article provides a method to specify sensor models for the environmental perception of automated driving systems.The key of the approach is a collaborative collection of causeeffect chains as the basis for specification. With this collection at hand, a tabular form is introduced to extract the relevance of the effect chains to be modeled. Combined profound expert assessments in the table enable the test engineer to specify sensor models within a traceable decision-making process.This work received funding from SET Level and VVM of the PEGASUS project family, promoted by the German Federal Ministry for Economic Affairs and Energy based on a decision of the Deutsche Bundestag.
In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the “ground truth” annotation is dictated by its accuracy. This paper presents a method for calibrating the accuracy of ground truth in practical applications in the automotive context. With an exemplary measurement device, we show that the proclaimed accuracy of the device is not always reached. However, test repetitions show deviations, resulting in non-uniform reliability and limited trustworthiness of the reference measurement. A similar result can be observed when reproducing the trajectory in the simulation environment: the exact reproduction of the driven trajectory does not always succeed in the simulation environment shown as an example because deviations occur. This is particularly relevant for making sensor-specific features such as material reflectivities for lidar and radar quantifiable in dynamic cases.
In virtual validation of automated driving, trustworthy simulation models of perception sensors are required. Radar sensors are particularly hard to model, as their measurements are notoriously difficult to interpret. This is due to their complex measurement principle, involving multi-path propagation of mm-waves, varying backscattering characteristics of objects, and further factors such as limited measurement ranges and resolutions that introduce uncertainty to the measurements. This work presents a method for studying the backscatter characteristics of vehicles under real-world driving conditions. A slalom-like driving scenario, which is representative for road-driving where the vehicle is visible under different aspect angles, has been designed. It aims at a high level of reproducibility of the trajectories driven by the vehicles, hence reducing additional sources of uncertainty that were otherwise present in the measurements. In a large-scale measurement campaign, 13 vehicles have been studied. The vehicles-under-test are observed by multiple radars, mounted at different heights, and carry reference sensors for obtaining their positions. In this paper, we present the measurement campaign and show major findings from our measurement results. Our focus lies on drawing conclusions for trustworthy sensor simulation. Both sensor measurement data, and MATLAB code for data analysis are made publicly available alongside this paper.
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