Adaptive Driving Agent Selection for Desired Aggressiveness
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Solution Overview
Problem
Current reinforcement learning systems for vehicle operation do not effectively adapt to varying driving environments and user preferences, particularly in terms of driving aggressiveness, as they lack a dynamic mechanism to select the most suitable agent based on real-time sensor data and user input.
Innovation Solution
A system that receives sensor data from a vehicle's environment and selects a reinforcement learning agent from a plurality based on a challenge score and desired driving style, allowing for adaptive driving actions to be generated, with the ability to automatically switch agents if the perceived environment changes, ensuring optimal operation across different driving conditions and user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single reinforcement learning agent is used for vehicle operation, then the system structure is simple, but the system cannot adapt to varying driving environments and user preferences
Solution Approach 1:
The system divides the reinforcement learning capability into multiple specialized agents, each trained for specific driving conditions (e.g., highway, urban, rainy) or user preferences (e.g., aggressive, conservative). Instead of one general agent, the system segments the problem into multiple focused agents that can be selectively applied based on the current situation.
Solution Approach 2:
The system implements dynamic agent selection by continuously evaluating sensor data and challenge scores to determine which agent is most appropriate for the current driving context. The agent selection is not static but dynamically adjusts based on real-time environmental conditions and user preferences.
2Adaptability or versatility
If multiple reinforcement learning agents are maintained for different driving conditions, then adaptability improves, but computational resources and processing time increase
Solution Approach 1:
Agents are pre-trained offline for specific driving conditions and scenarios. The challenging aspect of learning multiple agents is moved to the offline preparation phase, allowing the online system to simply select from pre-prepared agents based on current conditions, rather than learning in real-time.
Solution Approach 2:
The system replaces complex real-time evaluation of all possible agents with a challenge score calculation based on sensor data. This substitution uses a simplified scoring mechanism to quickly identify suitable agents without exhaustive comparison.
3Productivity
If reinforcement learning agents are trained for specific driving conditions, then driving performance improves, but the system complexity and training requirements increase
Solution Approach 1:
The system creates a universal agent selection framework that can handle multiple driving conditions and user preferences through a common architecture. The challenge score calculation and agent selection mechanism serve as a universal interface that works across all scenarios, reducing overall system complexity despite having multiple specialized agents.
Data Source
AI summary
A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive sensor data representing a perceived driving environment, select a reinforcement learning agent from a plurality of reinforcement learning agents based on a challenge score calculated using the sensor data and a desired driving style, and generate, via the selected reinforcement learning agent, a driving action based on the sensor data.