Lidar Fault Detection Using Historical Target Comparison
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Solution Overview
Problem
Lidar systems in autonomous vehicles face challenges in detecting faults such as damaged, dirty, or dislodged optical components, which can impair their ability to sense the environment accurately, leading to inaccurate data and impacting the vehicle's localization, perception, and motion planning operations.
Innovation Solution
A fault detection system compares incoming Lidar data with historical data using a fixed fault detection target to identify differences, initiating a fail-safe state if threshold conditions are met, thereby restricting operations until remediation is performed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Lidar systems operate continuously in changing environments, then productivity is improved, but reliability deteriorates due to difficulty in detecting fault conditions
Solution Approach 1:
A fault detection target is introduced as an intermediary object between the Lidar system and the environment. This target provides a known, stable reference that mediates the comparison between current and historical Lidar data, enabling reliable fault detection even when the surrounding environment is changing. The target acts as a controlled intermediary that isolates the detection process from environmental variables.
2Measurement precision
If fault detection compares incoming Lidar data with historical data, then measurement precision is improved, but device complexity increases due to additional comparison mechanisms
Solution Approach 1:
The fault detection mechanism extracts only the essential comparison function from the overall Lidar system. By isolating the comparison operation between incoming and historical data related to the fault detection target, the system achieves precise fault detection without requiring complex additional mechanisms. The extraction principle simplifies the system by focusing only on the critical comparison function.
3Reliability
If a fail-safe state is initiated upon fault detection, then reliability is improved, but productivity decreases due to operational restrictions
Solution Approach 1:
The system performs preliminary fault detection and comparison actions continuously in the background before critical failures occur. By detecting faults early through comparison with historical data and initiating fail-safe states proactively, the system ensures reliable operation while minimizing disruptions to productivity. The preliminary detection prevents catastrophic failures that would cause more significant operational interruptions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Ensures accurate detection of faults in Lidar systems, preventing the dissemination of potentially inaccurate data to downstream components and ensuring safe operation of autonomous vehicles by initiating a fail-safe state when faults are detected.
Implementation Method 1
each channel emits a laser signal into the environment that is reflected off of the surrounding environment back to the detector
Implementation Method 2
The Lidar unit also includes circuitry to measure the time of flight—i.e., the elapsed time from emitting the laser signal to detecting the return signal
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, and devices for fault detection in a Lidar system. A fault detection system obtains incoming Lidar data output by a Lidar system during operation of an AV system. The incoming Lidar data includes one or more data points corresponding to a fault detection target on an exterior of a vehicle of the AV system. The fault detection system accesses historical Lidar data that is based on data previously output by the Lidar system. The historical Lidar data corresponds to the fault detection target. The fault detection system performs a comparison of the incoming Lidar data with the historical Lidar data to identify any differences between the two sets of data. The fault detection system detects a fault condition occurring at the Lidar system based on the comparison.