The main objective of this article is to provide angle-dependent spectral reflectance measurements of various materials in the near infrared spectrum. In contrast to already existing reflectance libraries, e.g., NASA ECOSTRESS and Aster reflectance libraries, which consider only perpendicular reflectance measurements, the presented dataset includes an-gular resolution of the material reflectance. To conduct the angle-dependent spectral reflectance material measurements, a new measurement device based on a 945 nm time-of-flight camera is used, which was calibrated using Lambertian targets with defined reflectance values at 10, 50, and 95%. The spectral reflectance material measurements are taken for an angle range of 0 degrees to 80 degrees with 10 degrees incremental steps and stored in table format. The developed dataset is categorized with a novel material classification, divided into four different levels of detail considering material properties and distinguishing predominantly between mutually exclusive material classes (level 1) and material types (level 2). The dataset is published open access on the open repository Zenodo with record number 7467552 and version 1.0.1 [1] . Currently, the dataset contains 283 measurements and is continuously ex-tended in new versions on Zenodo. (c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/)

Virtual testing and validation are building blocks in the development of autonomous systems, in particular autonomous driving. Perception sensor models gained more attention to cover the entire tool chain of the sense–plan–act cycle, in a realistic test setup. In the literature or state-of-the-art software tools various kinds of lidar sensor models are available. We present a point cloud lidar sensor model, based on ray tracing, developed for a modular software architecture, which can be used stand-alone. The model is highly parametrizable and designed as a toolbox to simulate different kinds of lidar sensors. It is linked to an infrared material database to incorporate physical sensor effects introduced by the ray–surface interaction. The maximum detectable range depends on the material reflectivity, which can be covered with this approach. The angular dependence and maximum range for different Lambertian target materials are studied. Point clouds from a scene in an urban street environment are compared for different sensor parameters.

We present an assessment of simulated lidar point clouds based on different phenomenological range-reflectivity models. In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are derived from the lidar range equation, for clear and adverse weather conditions. The underlying parameters are the maximum ranges for best environment conditions, based on sensor datasheets, and a maximum range measurement for attenuation conditions. Furthermore, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. Applications are presented using a point cloud lidar model. Results of the point cloud comparison are demonstrated for a single scene or time step and an entire scenario of 40 time steps. When a reference point cloud is provided by the sensor manufacturer, feature validation becomes possible.

Reliable perception in smoke-filled tunnels is essential for rescue robots, yet the temporal evolution of LiDAR degradation under realistic smoke conditions is not well quantified. This paper investigates the degradation of LiDAR data induced by environmental factors in a full-scale tunnel experiment involving three smoke scenarios, with real combustion smoke and theatrical smoke. To enable consistent comparison across experiments with different smoke dynamics, an RGB-based visibility reference is first used for temporal alignment across measurements. Based on this alignment, physically interpretable LiDAR indicators, such as intensity attenuation and range-dependent point density loss, are used to characterize smoke-induced changes in the LiDAR data. In addition, an established Deep semi-supervised anomaly detection (DeepSAD) model is employed to derive a continuous data-driven degradation score that indicates deviations from nominal LiDAR range image patterns. The learned score remains stable under clear-air conditions, despite geometric variations caused by object and sensor movement. During smoke exposure, the score increases in all smoke scenarios, although the temporal evolution differs between scenarios. The results show that the degradation score derived from DeepSAD provides a continuous data-driven description of changes in LiDAR range images under smoke exposure. Overall, the study presents an experimental analysis of LiDAR degradation using full-scale tunnel experiments with smoke, reporting physically interpretable LiDAR indicators and a continuous data-driven degradation score.