Autonomous Vehicle Control Using Scenario-Specific Evaluation Modules
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Solution Overview
Problem
Autonomous vehicles face challenges in effectively navigating complex transportation networks due to the lack of efficient methods for real-time scenario-specific control, leading to potential safety and operational inefficiencies.
Innovation Solution
The implementation of a method where an autonomous vehicle uses scenario-specific operational control evaluation modules, such as Partially Observable Markov Decision Process models, to process sensor information and generate candidate vehicle control actions based on detected operational scenarios, enabling informed decision-making for safe and efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single general-purpose control system is used for autonomous vehicles, then device complexity is reduced, but the ability to handle diverse operational scenarios and measurement precision deteriorates
Solution Approach 1:
The control system is segmented into multiple specialized operational control evaluation modules, each dedicated to a specific operational scenario (e.g., pedestrian crossing, intersection navigation, highway merging). This segmentation allows each module to achieve high measurement precision for its designated scenario while the overall system manages complexity through modular organization rather than a single monolithic structure.
2Productivity
If multiple scenario-specific control modules are implemented, then operational efficiency and safety improve, but device complexity increases
Solution Approach 1:
The system employs a universal framework architecture that can instantiate multiple scenario-specific modules. The framework provides common functionalities (sensor integration, action execution, scenario detection) that serve all specialized modules, allowing the system to achieve high operational efficiency through scenario-specific processing while managing complexity through shared universal components.
Solution Approach 2:
The control system dynamically selects and activates appropriate scenario-specific modules based on real-time operational conditions detected by sensors. This dynamic adaptation allows the system to maintain high operational efficiency by using only the necessary modules for current scenarios, rather than continuously running all modules, thereby managing overall system complexity.
3Reliability
If real-time sensor processing is performed, then operational safety improves, but energy consumption increases
Solution Approach 1:
The system performs partial processing by activating only the specific operational control evaluation modules needed for current scenarios rather than processing all possible scenarios continuously. This partial action approach maintains operational safety by thoroughly processing relevant scenarios while reducing overall computational energy consumption by excluding irrelevant processing.
Data Source
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AI summary
Autonomous vehicle operational management may include traversing, by an autonomous vehicle, a vehicle transportation network. Traversing the vehicle transportation network may include receiving, from a sensor of the autonomous vehicle, sensor information corresponding to an external object within a defined distance of the autonomous vehicle, identifying a distinct vehicle operational scenario in response to receiving the sensor information, instantiating a scenario-specific operational control evaluation module instance, wherein the scenario-specific operational control evaluation module instance is an instance of a scenario-specific operational control evaluation module modeling the distinct vehicle operational scenario, receiving a candidate vehicle control action from the scenario-specific operational control evaluation module instance, and traversing a portion of the vehicle transportation network based on the candidate vehicle control action.