Sensor Platform Thermal Prediction for Autonomous Vehicle Overheating
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Solution Overview
Problem
Autonomous vehicles face challenges in managing thermal loads of sensors and computing components, particularly in warmer environments, which can lead to overheating and potential shutdowns, causing operational degradation and safety issues.
Innovation Solution
A computer-implemented method and system that uses a thermal model to predict temperature conditions of sensor platforms based on local weather data and routing information, sending warnings and reporting degraded states to prevent overheating by proactively identifying and mitigating thermal shutdowns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If sensors and computing components are packed in a small space to reduce vehicle size, then device compactness is improved, but thermal management becomes more difficult leading to overheating
Solution Approach 1:
The thermal model performs preliminary thermal analysis before the vehicle operates in challenging conditions. By predicting future thermal states based on weather data, routing information, and component specifications, the system proactively identifies potential overheating scenarios and triggers preventive actions such as activating cooling systems or modifying operational parameters before temperatures reach critical levels.
Solution Approach 2:
The system continuously monitors actual temperature sensors and compares readings against predicted thermal model values. This feedback loop validates the thermal model's accuracy and enables real-time adjustments to cooling strategies. When deviations are detected, the system dynamically modifies cooling fan speeds, pump flows, or operational parameters to maintain thermal safety margins.
2Temperature
If cooling systems are activated continuously to prevent overheating, then temperature control is improved, but energy consumption increases
Solution Approach 1:
The thermal model predicts future thermal states and triggers cooling actions only when and where needed. By analyzing weather forecasts, routing data, and component thermal characteristics, the system anticipates overheating risks and activates cooling systems proactively during specific high-risk periods rather than continuously, significantly reducing energy consumption while maintaining effective temperature control.
Solution Approach 2:
The cooling system operates dynamically with variable intensity based on real-time thermal conditions and predictions. The system adjusts cooling fan speeds, pump flows, and other cooling parameters dynamically rather than maintaining constant high-level operation. This dynamic control optimizes the balance between temperature management and energy consumption by applying only the necessary cooling power at each moment.
3Reliability
If thermal monitoring is performed in real-time to prevent overheating, then system reliability is improved, but computational load increases
Solution Approach 1:
The thermal model performs comprehensive thermal analysis in advance by processing weather data, routing information, and component specifications before the vehicle encounters challenging thermal conditions. This preliminary computational work reduces the need for intensive real-time calculations, as the model has already identified potential thermal risks and optimal cooling strategies that can be executed with minimal real-time computational overhead.
Solution Approach 2:
The system uses a simplified thermal model that replicates complex thermal behavior through pre-computed lookup tables and simplified equations rather than running full-scale thermal simulations in real-time. This copied approximation maintains sufficient accuracy for safety-critical decisions while dramatically reducing computational power requirements compared to detailed finite element analysis or other intensive simulation methods.
Data Source
AI summary
A computer-implemented method may include receiving, by a computing system on an autonomous vehicle, local weather data and routing data of the autonomous vehicle, the autonomous vehicle including a sensor platform mounted on the autonomous vehicle. The method may include based on the local weather data and the routing data, predicting, by the computing system using a thermal model, a thermal loading and a temperature of a component(s) of a sensor platform at one or more future times, the thermal loading and the temperature being during an operation of the autonomous vehicle. The method may include sending an overheating warning to a controller of the autonomous vehicle based on a determination that the thermal loading or the temperature of the component(s) exceeds a threshold. The method may include reporting a degraded state of the autonomous vehicle to park prior to a predicted overheating of the component(s).