Autonomous Vehicle RL Decisions With Uncertainty Gating
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Solution Overview
Problem
Existing reinforcement learning (RL) methods for autonomous vehicles lack the ability to estimate the uncertainty of their decisions, leading to potential unsafe outcomes when faced with unfamiliar situations.
Innovation Solution
The proposed method involves using an RL agent that assesses the uncertainty of its decisions by evaluating the variability of multiple state-action value functions, allowing the agent to execute decisions only if the uncertainty is below a predefined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning methods are used for autonomous vehicle decision-making, then the agent can handle diverse environments and interactions, but the agent cannot estimate uncertainty of its decisions
Solution Approach 1:
The patent segments the decision-making process into two distinct components: a reinforcement learning agent for generating decisions and an uncertainty estimator for assessing confidence. This segmentation allows the system to maintain the adaptability of RL while adding reliable uncertainty estimation through separate value function evaluations and variability calculations.
Solution Approach 2:
The patent introduces an intermediary uncertainty estimation mechanism that acts as a mediator between the RL agent's decision output and the final execution. By calculating variability in value function estimates, this intermediary layer provides confidence assessment without altering the core RL decision-making process, resolving the contradiction between adaptability and reliability.
2Productivity
If the agent outputs decisions in all situations, then continuous decision-making is maintained, but unsafe decisions may be executed in unfamiliar situations
Solution Approach 1:
The patent implements feedback by continuously monitoring the uncertainty/variability of value function estimates and using this information to gate decision execution. When variability exceeds thresholds, the system feedbacks to inhibit execution, preventing unsafe decisions while maintaining continuous decision-making capability in familiar situations.
Solution Approach 2:
The patent applies beforehand cushioning by pre-establishing uncertainty thresholds and safety criteria before deployment. These pre-defined safety margins act as cushioning protection, allowing the system to maintain productivity in familiar situations while automatically preventing execution when uncertainty indicates potential safety issues.
3Reliability
If comprehensive real-world testing is performed, then the agent's error level is reduced, but resources are drained from other R&D aspects
Solution Approach 1:
The patent enables self-service by allowing the uncertainty estimation mechanism to automatically identify and flag potentially unsafe situations without requiring extensive manual testing. The system serves itself by using its own value function variability to assess safety, reducing the need for resource-intensive real-world testing while maintaining low error levels.
Solution Approach 2:
The patent substitutes mechanical testing processes with a computational uncertainty estimation system. Instead of relying on extensive physical real-world testing to reduce errors, the system uses mathematical variability analysis of value functions to identify unsafe situations, dramatically reducing R&D resource consumption while maintaining or improving reliability.
Data Source
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AI summary
A method (100) of controlling an autonomous vehicle using a reinforcement learning, RL, agent, the method comprising: a plurality of training sessions (110-1,..., 110-K), in which the RL agent interacts with an environment including the autonomous vehicle, each training session having a different initial value and yielding a state-action value function Q k (s, a) dependent on state and action; decision-making (112), in which the RL agent outputs at least one tentative decision relating to control of the autonomous vehicle; uncertainty estimation (114) on the basis of a variability measure for the plurality of state-action value functions evaluated for a state-action pair corresponding to each of the tentative decisions; and vehicle control (116), wherein the at least one tentative decision is executed in dependence of the estimated uncertainty.