In-Vehicle LIDAR Dirt Detection for Reliable Automated Control
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Solution Overview
Problem
Existing vehicle recognition systems face performance degradation due to dirt attachment on LIDAR devices, which is not effectively addressed by existing methods, leading to unreliable automated vehicle control.
Innovation Solution
A vehicle recognition device that acquires a parameter value representing the degree of dirtiness of an in-vehicle LIDAR and judges its state as abnormal if the value exceeds a threshold continuously over a specified period, with varying thresholds for different sections of the LIDAR's detection range, allowing for timely intervention in automated driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated control of vehicle is performed using LIDAR, then productivity is improved, but reliability deteriorates due to dirt attachment on LIDAR
Solution Approach 1:
The system performs preliminary detection of dirt attachment on the LIDAR before it significantly degrades measurement performance. By continuously monitoring parameters such as detection object distance, reflection intensity, and detection frequency, the system identifies dirt accumulation early and triggers washing control, preventing reliability deterioration while maintaining automated control productivity.
2Reliability
If dirt detection parameter threshold is set low, then reliability is improved, but false abnormal state judgment increases
Solution Approach 1:
The system merges multiple dirt detection parameters (detection object distance, reflection intensity, detection frequency) and evaluates them collectively against threshold values. This combined evaluation approach improves detection reliability by considering multiple indicators simultaneously, while reducing false abnormal state judgments through comprehensive parameter analysis rather than relying on a single parameter.
Solution Approach 2:
The system dynamically adjusts threshold values for dirt detection parameters based on driving conditions, vehicle speed, and environmental factors. By changing parameters adaptively rather than using fixed thresholds, the system maintains high detection reliability across varying conditions while minimizing false abnormal state judgments.
3Reliability
If continuous parameter monitoring is performed, then reliability is improved, but use of energy increases
Solution Approach 1:
The system performs periodic monitoring of dirt detection parameters at predetermined intervals rather than continuous monitoring. This periodic evaluation approach maintains sufficient detection reliability by checking parameters at appropriate intervals, while significantly reducing energy consumption compared to continuous monitoring. The system triggers washing control only when parameter changes exceed thresholds, optimizing the balance between reliability and energy usage.
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
This solution effectively inhibits performance degradation of automated vehicle control by detecting and addressing dirt attachment on LIDAR devices, ensuring reliable operation by adjusting the drive mode to impose higher tasks on the driver when an abnormal state is detected.
Implementation Method 1
a light detection and ranging (LIDAR) attached to the vehicle. The LIDAR emits light (or electromagnetic waves having a wavelength close to light), measures scattered light thereof, and measures a distance to a target on the basis of a time from light emission to light reception
Implementation Method 2
measures a distance to a target on the basis of a time from light emission to light reception
Data Source
AI summary
A vehicle recognition device includes: an acquisition unit configured to acquire a value of a parameter relating to a degree of dirtiness of an in-vehicle LIDAR device detecting objects using light or a radiowave close to light; and a judgment unit configured to judge a state of the in-vehicle LIDAR device to be an abnormal state in a case in which the value of the parameter continuously exceeds a threshold during a first period, and the amount of change of the value of the parameter in a second period including a part or whole of the first period is within a reference range.