Risk-Aware AV Executor for Context-Based Action Selection
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Solution Overview
Problem
Autonomous vehicles face challenges in selecting appropriate control actions due to limited information about scenario-specific operational control evaluation modules (SSOCEMs) and reliance on human intervention, leading to suboptimal operations and safety concerns.
Innovation Solution
Implementing a system where the autonomous vehicle operational management controller can reason about the type of SSOCEM providing candidate actions and consider contextual information, such as sensor and actuator states, to select control actions, and allowing SSOCEMs to transmit sets of candidate actions rather than single actions, enabling prioritization and sequential decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle relies on SSOCEMs to provide control actions, then the system can operate autonomously, but the limited information about SSOCEM competence and context leads to suboptimal action selection
Solution Approach 1:
The executor implements a feedback mechanism by receiving confidence scores from SSOCEMs and using this information to evaluate and select among candidate actions. The system feeds back the selected action to SSOCEMs, creating a closed-loop information flow that allows the executor to make informed decisions based on SSOCEM competence indicators rather than blindly following their recommendations.
Solution Approach 2:
The executor serves as an intermediary layer between SSOCEMs and the vehicle control system. It receives multiple candidate actions from different SSOCEMs, evaluates them based on confidence scores and contextual information, and selects the most appropriate action. This intermediary role resolves the information asymmetry by centralizing the decision-making process with access to comprehensive SSOCEM performance data.
2Device complexity
If SSOCEMs provide single control actions, then the decision-making process is simple, but the system lacks the ability to prioritize and consider multiple options, leading to suboptimal operations
Solution Approach 1:
The system dynamically adjusts the decision-making process by receiving multiple candidate actions from SSOCEMs rather than fixed single actions. The executor can adaptively select among multiple options based on current contextual factors and SSOCEM confidence scores, allowing the system to optimize operational efficiency while maintaining manageable complexity through structured evaluation.
Solution Approach 2:
Instead of requiring SSOCEMs to provide complete and perfect single actions, the system accepts multiple partial candidate actions from different SSOCEMs. This excessive provision of candidate actions (more than strictly necessary) allows the executor to select the optimal action through comparison and evaluation, improving operational efficiency without proportionally increasing system complexity.
3Reliability
If the system requires human intervention for action selection, then safety can be ensured through human judgment, but operational efficiency decreases due to reliance on external input
Solution Approach 1:
The executor implements self-service by autonomously selecting control actions based on confidence scores and contextual information from SSOCEMs, eliminating the need for continuous human intervention. The system serves itself by evaluating multiple candidate actions and making informed decisions independently, thereby maintaining safety through systematic evaluation while improving operational efficiency through autonomous operation.
Solution Approach 2:
The system performs preliminary action by pre-evaluating multiple candidate actions from SSOCEMs and selecting the optimal action before execution. This advance evaluation and selection process ensures safety through comprehensive assessment while enabling efficient autonomous operation without requiring real-time human input during critical decision moments.
Data Source
AI summary
A first distinct vehicle operational scenario is identified for an autonomous vehicle (AV). A first set of candidate vehicle control actions are received from a model that provides a first solution to the first distinct vehicle operational scenario. An action is selected from the first set of candidate vehicle control actions. The AV is controlled based on the action. The first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV.