ReviewSensors (Basel, Switzerland)2020
Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Decentralized fault-tolerant control of multi-mobile robot system addressing LiDAR sensor faults.Scientific reports · 2024Article
- Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion.Sensors (Basel, Switzerland) · 2023Article
- A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors.Sensors (Basel, Switzerland) · 2023Review
- Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors.Sensors (Basel, Switzerland) · 2022Article
- Trends in Sensors Fault Diagnosis.Sensors (Basel, Switzerland) · 2021Article
- Considerations for Determining the Coefficient of Inertia Masses for a Tracked Vehicle.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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