Characterization and simulation of the effect of road dirt on the performance of a laser scanner

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.

Section: Literature Survey On Fdiir Methods For Automotive Lidarmentioning

confidence: 99%

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

Goelles

Schlager

Muckenhuber

2020

“…Changes in its chemical composition due to constant exposure to ultraviolet light [ 8 ]. New layers formed on the LIDAR cover such as: dirt layers [ 9 ], water layers (or ice layers) deposited due to the presence of rain, fog, insects and spray from other vehicles. Scattering or absorption caused by rain drops, exhaust gasses or other pollutants [ 10 ] and dust [ 11 ].…”

Section: State Of the Artmentioning

confidence: 99%

“…New layers formed on the LIDAR cover such as: dirt layers [ 9 ], water layers (or ice layers) deposited due to the presence of rain, fog, insects and spray from other vehicles.…”

Section: State Of the Artmentioning

confidence: 99%

Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere

Rivero

Gerbich

Teiluf

et al. 2020

Self Cite

A semi-/autonomous driving car requires local weather information to identify if it is working inside its operational design domain and adapt itself accordingly. This information can be extracted from changes in the detections of a light detection and ranging (LIDAR) sensor. These changes are caused by modifications in the volumetric scattering of the atmosphere or surface reflection of objects in the field of view of the LIDAR. In order to evaluate the use of an automotive LIDAR as a weather sensor, a LIDAR is placed outdoor in a fixed position for a period of 9 months covering all seasons. As target, an asphalt region from a parking lot is chosen. The collected sensor raw data is labeled depending on the occurring weather conditions as: clear, rain, fog and snow, and the presence of sunlight: with or without background radiation. The influence of different weather types and background radiations on the measurement results is analyzed and different parameters are chosen in order to maximize the classification accuracy. The classification is done per frame in order to provide fast update rates while still keeping an F1 score higher than 80%. Additionally, the field of view is divided into two regions: atmosphere and street, where the influences of different weather types are most notable. The resulting classifiers can be used separately or together increasing the versatility of the system. A possible way of extending the method for a moving platform and alternatives to virtually simulate the scene are also discussed.

“…Recently, LIDAR sensors have also been used mostly due to their high resolution, ability to work under low light conditions and direct distance measurement [ 4 , 5 ]. However, the high resolution obtained by using infrared light comes at the cost of a reduced performance when dirt [ 6 ], rain, fog [ 7 ], snow [ 8 ], exhaust gases [ 9 ] and spray water [ 10 ] are present. Besides reducing range, these phenomena cause extra detections which can have negative effects like phantom braking.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations

Rivero

Gerbich

Buschardt

et al. 2021

Self Cite

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.