Vehicle Resource Allocation by Driving Scenario and Sensor Load
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently managing computational resources based on varying environmental scenarios, leading to suboptimal power consumption and communication bandwidth usage.
Innovation Solution
Implementing a scenario-based resource management system that utilizes machine learning models to identify scenarios and dynamically allocate computational resources, such as processor power and sensor operations, to match the demands of the vehicle's environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational resources are continuously allocated at maximum capacity, then the vehicle can handle any scenario demands, but power consumption increases and battery life decreases
Solution Approach 1:
The system dynamically adjusts computational resource allocation based on real-time scenario classification. The scenario recognition system continuously monitors environmental conditions and modifies processor power, sensor operations, and communication bandwidth accordingly, transitioning from static maximum capacity allocation to adaptive dynamic allocation that matches actual demand
Solution Approach 2:
The system changes operational parameters of computational resources based on scenario type. Different scenarios trigger different resource allocation parameters, such as processor frequency, sensor sampling rates, and communication protocols, optimizing the balance between handling capability and power consumption for each specific scenario
2Use of energy by moving object
If computational resources are reduced to save power, then battery life extends, but the vehicle may fail to handle complex scenarios adequately
Solution Approach 1:
The system ensures reliability is maintained by dynamically scaling resources upward when complex scenarios are detected. The scenario recognition system identifies high-demand situations and automatically increases computational resource allocation to appropriate levels, ensuring adequate handling capability is preserved when needed
Solution Approach 2:
The system uses feedback from scenario recognition to continuously monitor environmental complexity and adjust resource allocation accordingly. This closed-loop control ensures that power reduction does not compromise reliability, as the system responds to actual scenario demands in real-time
3Measurement precision
If all sensors and processors operate at full capacity continuously, then accurate environmental perception is maintained, but communication bandwidth is wasted during simple scenarios
Solution Approach 1:
The system applies different operational qualities to different computational components based on scenario requirements. Instead of uniformly operating all sensors and processors at full capacity, the system selectively adjusts individual component performance levels according to the specific demands of the current scenario
Solution Approach 2:
The system employs partial action by activating only the necessary subset of sensors and processing capabilities required for each scenario. Rather than running all components at full capacity, the system applies just enough computational power to adequately perceive and respond to the environmental conditions
4Reliability
If the vehicle uses high computational resources in all scenarios, then safety is maximized, but operational efficiency decreases
Solution Approach 1:
The system dynamically optimizes the safety-efficiency tradeoff by adjusting resource allocation in real-time based on scenario risk levels. High-risk scenarios receive maximum computational resources for enhanced safety, while low-risk scenarios use reduced resources to improve operational efficiency
Solution Approach 2:
The system changes operational parameters to optimize the safety-efficiency balance. By modifying processor power, sensor activation, and communication protocols according to scenario classification, the system achieves adequate safety margins while maximizing operational efficiency across different driving conditions
Data Source
AI summary
Provided are methods for managing vehicle resources based on scenarios, which can include obtaining, from the at least one sensor, information representative of the environment of the vehicle; determining, based on the information representative of the environment of the vehicle, a current scenario of the environment of the vehicle; determining, based on the determined current scenario, a level of computational resources appropriate for the determined current scenario; and adjusting at least one parameter associated with the at least one sensor based on the determined level of computational resources. Systems and computer program products are also provided.