Vehicle Sensor Cleaning Control for Adaptive Fluid Use
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Solution Overview
Problem
Existing vehicle sensor cleaning systems lack an efficient and operator-adjustable mechanism to determine the need for cleaning based on sensor dirtiness and environmental conditions, often leading to suboptimal cleaning frequency and fluid usage.
Innovation Solution
A computer-controlled cleaning system that adjusts its cleaning plan based on operator inputs and sensor data, using a machine-learning program to determine parameters such as dirtiness thresholds, cleaning durations, and fluid usage, ensuring effective and efficient cleaning of vehicle sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the cleaning system operates frequently to maintain sensor cleanliness, then sensor performance is improved, but fluid consumption increases
Solution Approach 1:
The system uses sensor data and environmental information as feedback to dynamically adjust cleaning frequency and fluid usage. The machine learning program continuously learns from cleaning outcomes and operator responses to optimize the balance between maintaining sensor performance and minimizing fluid consumption.
Solution Approach 2:
The cleaning plan is dynamically adjusted based on varying environmental conditions, sensor dirtiness levels, and operator preferences. The system transitions from static cleaning schedules to adaptive cleaning strategies that respond to real-time conditions, optimizing both sensor performance and fluid efficiency.
2Loss of substance
If the cleaning system uses minimal fluid to meet operator expectations, then fluid consumption is reduced, but cleaning effectiveness may be compromised
Solution Approach 1:
The system applies partial cleaning actions when conditions permit, using just enough fluid to achieve acceptable cleanliness levels rather than always applying full cleaning cycles. This approach reduces fluid consumption while maintaining cleaning effectiveness through targeted, condition-based cleaning interventions.
Solution Approach 2:
The machine learning program adjusts cleaning parameters such as fluid quantity, spray duration, and cleaning intensity based on sensor data and environmental conditions. By dynamically changing these parameters, the system optimizes cleaning effectiveness while minimizing fluid usage.
3Productivity
If the system automatically adjusts cleaning parameters based on environmental conditions, then cleaning efficiency is improved, but system complexity increases
Solution Approach 1:
The machine learning program enables the system to automatically learn and adjust cleaning parameters without requiring complex manual configuration or intervention. The system serves itself by continuously improving its cleaning strategy through learning from past cleaning outcomes and environmental patterns, reducing the need for complex external control mechanisms.
Data Source
AI summary
A computer includes a processor and a memory, and the memory stores instructions executable to actuate a cleaning system to spray a sensor in accordance with a cleaning plan stored in the memory, actuate the cleaning system to spray the sensor in response to an input from an operator, and adjust the cleaning plan based on the input from the operator to actuate the cleaning system.