Autonomous Vehicle Sensor Reliability Assessment
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Solution Overview
Problem
Current autonomous vehicle systems require expensive and robust sensors to maintain reliability across all conditions, leading to high equipment and maintenance costs, and potential vehicle downtime due to sensor limitations and environmental vulnerabilities.
Innovation Solution
A system and method for assessing vehicle system reliability using data from sensors and diagnostic sensors, allowing the autonomous driving module to adjust operation modes based on collected condition data, including environmental and vehicle system status, to optimize sensor usage and reduce reliance on expensive sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expensive robust sensors such as LIDAR are used to maintain reliability under all conditions, then sensor reliability is improved, but equipment cost and maintenance cost increase significantly
Solution Approach 1:
The patent implements dynamic sensor selection and reliability assessment that adapts to changing environmental conditions. The system dynamically determines which sensors to use based on current conditions (e.g., using cameras in good weather, LIDAR in adverse weather), rather than relying on expensive robust sensors continuously. This dynamic approach maintains reliability while reducing equipment and maintenance costs.
Solution Approach 2:
The system changes operational parameters by selecting different sensors based on environmental conditions. Instead of using a fixed expensive sensor configuration, the system varies sensor selection (camera, LIDAR, radar, ultrasound) according to conditions such as weather, lighting, and contamination levels, thereby maintaining reliability without requiring expensive robust sensors for all conditions.
2Reliability
If all vehicle systems are required to meet a high threshold for reliability in all conditions, then vehicle safety is improved, but maintenance cost and vehicle downtime increase
Solution Approach 1:
The patent implements dynamic reliability assessment that evaluates vehicle system reliability in real-time based on current environmental and operational conditions. The autonomous driving module dynamically adjusts operation modes according to assessed reliability levels, allowing the vehicle to operate continuously by adapting to conditions rather than shutting down for maintenance unless truly necessary.
Solution Approach 2:
The system incorporates continuous feedback loops where sensor data is collected, reliability is assessed, and operation modes are adjusted accordingly. This feedback mechanism allows the vehicle to monitor its own reliability status and make real-time adjustments, reducing unplanned downtime and optimizing maintenance scheduling based on actual condition rather than fixed schedules.
3Reliability
If LIDAR systems are used to improve autonomous driving reliability, then robustness is improved, but performance degrades in adverse weather conditions due to wavelength limitations
Solution Approach 1:
The patent implements a multi-functional sensor system that can operate across diverse environmental conditions. By incorporating multiple sensor types (camera, LIDAR, radar, ultrasound) each with different strengths, the system achieves universal operability - cameras work well in good weather, LIDAR provides depth information, radar penetrates weather conditions, and ultrasound detects close objects. The system selects and combines sensors based on current conditions to maintain reliability.
Solution Approach 2:
The system uses an intermediary reliability assessment module that mediates between environmental conditions and sensor selection. This intermediary evaluates conditions and determines the appropriate sensor configuration, acting as a bridge that translates environmental state into optimal sensor usage, thereby overcoming the limitations of individual sensors in specific conditions.
Data Source
AI summary
A system, method, and processor-readable medium for assessing the reliability of vehicle systems used in an autonomous vehicle. The assessment may be performed at least in part on the basis of data collected by one or more of the vehicle's sensors. The result of the assessment may be used as the basis for decisions about vehicle operation carried out by an autonomous driving module.