Multi-Objective Policy Interface for Autonomous Vehicle Control
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Solution Overview
Problem
Autonomous vehicles face limitations in identifying and managing distinct vehicle operational scenarios due to limited resources and single-objective decision-making, which neglects risk, safety, social acceptability, and passenger preferences.
Innovation Solution
A method and apparatus for scenario-specific operational control management using a multi-objective policy that includes multiple objectives and priorities, allowing for user interface feedback to adapt and update vehicle control actions, enabling better decision-making in traversing vehicle transportation networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single objective reasoning is used in decision making, then the decision-making process is simple and fast, but the system cannot consider riskiness, safety, social acceptability, or passenger preferences
Solution Approach 1:
The decision-making process is segmented into multiple independent objective functions (safety, riskiness, social acceptability, passenger preferences) that are evaluated separately and then aggregated. This allows the system to maintain computational efficiency while considering multiple factors, as each objective can be computed independently and then combined through weighted summation or other aggregation methods.
Solution Approach 2:
The system transitions from single-objective to multi-objective decision-making by adding dimensional complexity to the evaluation space. Instead of optimizing along a single dimension, the system evaluates decisions across multiple dimensions (safety, risk, social acceptability, preferences) simultaneously, enabling comprehensive consideration of all factors while maintaining a structured approach to balancing competing objectives.
2Reliability
If multiple objectives and priorities are integrated into the policy, then safety, social acceptability, and passenger preferences are improved, but the system complexity and computational resources increase
Solution Approach 1:
The system employs dynamic priority assignment where the importance of different objectives can change based on the operational context. For example, safety may be assigned higher priority in critical situations while passenger preferences may be weighted more heavily in routine operations. This dynamic approach allows the system to maintain high reliability across diverse scenarios without requiring a static complex structure for all possible situations.
Solution Approach 2:
The system changes parameters such as objective weights, priority levels, and constraint thresholds based on the specific operational scenario and context. By adjusting these parameters dynamically, the system can adapt to different situations and maintain appropriate safety and social acceptability standards without requiring a fundamentally different system architecture for each scenario, thus managing complexity through parameter adjustment rather than structural complexity.
3Measurement precision
If user interface feedback is used to adapt and update vehicle control actions, then passenger preferences and control accuracy are improved, but the response time and computational load increase
Solution Approach 1:
The system performs preliminary computations and preparations for potential control actions in advance, so that when user feedback is received, the system can quickly select and execute the appropriate pre-evaluated action. This reduces the real-time computational burden and response time while maintaining high control accuracy, as the heavy lifting of evaluating multiple objectives has already been done for various possible scenarios.
Solution Approach 2:
The system implements a feedback mechanism where user interface inputs are continuously incorporated to adapt and refine control actions. This feedback loop allows the system to learn from user preferences and adjustments, improving control accuracy over time. The feedback is processed efficiently by comparing user inputs against pre-computed objective evaluations and making incremental adjustments rather than re-evaluating all objectives from scratch.
Data Source
AI summary
A vehicle traversing a vehicle transportation network may use a scenario-specific operational control evaluation model instance. A multi-objective policy for the model is received, wherein the policy includes at least a first objective, a second objective, and a priority of the first objective relative to the second objective. A representation of the policy (e.g., the first objective, the second objective, and the priority) is generated using a user interface. Based on feedback to the user interface, a change to the multi-objective policy for the scenario-specific operational control evaluation model is received. The change is to the first objective, the second objective, the priority, of some combination thereof. Then, for determining a vehicle control action for traversing the vehicle transportation network, an updated multi-objective policy for the scenario-specific operational control evaluation model is generated to include the change to the policy.