Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery.

Surface melt in the ablation zone is dominated by atmospheric temperature and surface albedo. We developed a surface mass-balance model with a dynamic component of glacier ice albedo which includes surface properties, clouds and the angle of the sun. The ice albedo reduction is mainly caused by impurity accumulation of non-biological origin such as dust and black carbon (BC), which is currently not included in other surface mass-balance models. Simulations show that dust from meltout is the main source of impurity mass at the melting glacier ice surface, and current rates of atmospheric deposition of dust play only a minor role. However, for BC the atmospheric deposition is the main source where ice melt rates are below 1 m, and atmospheric deposition is most likely from intercontinental transport due to the scarce population and lack of forests in Greenland.

Lidar is an important component of the perception suite for automated systems. The effects of vibration on lidar point clouds are mostly unknown, despite the lidar’s wide adaption and usual application under conditions where vibration occurs frequently. In this study, we performed controlled vibration tests from 6 to 2000 Hz at 9 and 12 m/s ² in vertical direction on the automotive lidar OS1-64 by Ouster. An information loss emerged which is mostly independent from frequency and acceleration. The loss of points is randomly distributed and does not correlate with range, intensity, or ring number (the horizontal line of the rotating lidar unit). The resonance frequency of 1426 Hz proved to be unproblematic as no pronounced negative effects on the point cloud could be identified. For vibration detection, the internal Inertial Measurement Unit (IMU) of the OS1-64 is accurate and sufficient for vibrations up to 50 Hz. Above 50 Hz, external IMUs would be required for vibration detection. Counting the number of points on a target close to the edges was investigated as an exemplary way to detect vibration purely based on the point cloud, i.e., independent of the lidar’s IMU.

Albedo is the dominant factor governing surface melt variability in the ablation area of ice sheets and glaciers. Aerosols such as mineral dust and black carbon (soot) accumulate on the ice surface and cause a darker surface and therefore a lower albedo. The darkening effect on the ice surface is currently not included in sea level projections, and the effect is unknown. We present a model framework which includes ice dynamics, aerosol transport, aerosol accumulation and the darkening effect on ice albedo and its consequences for surface melt. The model is applied to a simplified geometry resembling the conditions of the Greenland ice sheet, and it is forced by several temperature scenarios to quantify the darkening effect of aerosols on future mass loss. The effect of aerosols depends non-linearly on the temperature rise due to the feedback between aerosol accumulation and surface melt. According to our conceptual model, accounting for black carbon and dust in future projections of ice sheet changes until the year 3000 could induce an additional volume loss of 7 %. Since we have ignored some feedback processes, the impact might be even larger.

Lidar sensors play an essential role in the perception system of automated vehicles. Fault Detection, Isolation, Identification, and Recovery (FDIIR) systems are essential for increasing the reliability of lidar sensors. Knowing the influence of different faults on lidar data is the first crucial step towards fault detection for lidar sensors in automated vehicles. We investigate the influences of sensor cover contaminations on the output data, i.e., on the lidar point cloud and full waveform. Different contamination types were applied (dew, dirt, artificial dirt, foam, water, and oil) and the influence on the output data of the single beam lidar RIEGL LD05-A20 and the automotive mechanically spinning lidar Ouster OS1-64 was evaluated. The LD05-A20 measurements show that dew, artificial dirt, and foam lead to unwanted reflections at the sensor cover. Dew, artificial dirt over the entire transmitter, and foam measurements lead to severe faults, i.e., complete sensor blindness. The OS1-64 measurements also show that dew can lead to almost complete sensor blindness. The results look promising for further studies on fault detection and isolation, since the different contamination types lead to different symptom combinations.

Modelling the evolution of the Earth system on long timescales requires the knowledge and understanding of driving mechanisms, such as the hydrological cycle. This is dominant in all components of the Earth’s system,\ud such as atmosphere, ocean, land surfaces/vegetation and the cryosphere. Observations and measurements of stable water isotopes in climate archives can help to decipher and reconstruct climate change and its regional variations. For the cryosphere, the δ18O cycle in the current generation of Earth-System-Models is missing and an efficient and accurate tracer transport scheme is required. We describe ISOPOLIS 1.0 a modular semi-Lagrangian transport scheme of second-order accuracy, which is coupled to the polythermal and thermomechanical ice sheet model SICOPOLIS (version 2.9). Model skill is demonstrated by experiments with a simplified ice sheet geometry and by comparisons of simulated ice cores with data from Greenland (GRIP) and Antarctica\ud (Vostok). The presented method is a valuable tool to investigate the transport of any kind of passive tracer inside a polythermal ice sheet and is an important step to model the whole δ18O cycle