Autonomous Navigation System Intention Recognition
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Solution Overview
Problem
Current autonomous navigation systems for vehicles lack the ability to accurately predict and navigate around dynamic elements in complex environments, often relying on extrapolated motion patterns that may lead to unsafe intersections with pedestrians and other vehicles.
Innovation Solution
An autonomous navigation system that identifies contextual cues associated with dynamic elements, such as pedestrians and vehicles, to predict their trajectories and generate control commands that avoid intersections, using sensor data and intention associations to determine safe driving routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If autonomous navigation systems use extrapolated motion patterns to predict trajectories of dynamic elements, then the system complexity is reduced, but the prediction accuracy and safety deteriorate
Solution Approach 1:
The system segments trajectory prediction into multiple independent models: constant velocity model, constant acceleration model, and lane change model. Each model handles specific motion patterns separately, allowing the system to select the appropriate model based on detected contextual cues without requiring a single complex unified model.
Solution Approach 2:
The system changes prediction parameters dynamically by selecting different motion models based on detected contextual cues. When a pedestrian is detected near the roadway, the system switches to a more conservative prediction model that accounts for potential sudden movements, thereby improving prediction accuracy without permanently increasing system complexity.
2Reliability
If autonomous navigation systems detect and respond to all dynamic elements in real-time, then collision safety is improved, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary detection and classification of dynamic elements into predefined categories (pedestrians, vehicles, animals, etc.) with associated motion models. By pre-establishing these categories and their typical motion patterns, the system reduces the computational burden during real-time operation, allowing faster response while maintaining comprehensive safety monitoring.
Solution Approach 2:
The system applies different levels of monitoring intensity to different dynamic elements based on their risk level. High-priority elements (pedestrians near roadway, vehicles in blind spots) receive continuous detailed tracking with multiple prediction models, while lower-priority elements receive periodic monitoring, thereby reducing overall processing time while maintaining collision avoidance capability.
Data Source
AI summary
Some embodiments provide an autonomous navigation system which autonomously navigates a vehicle through an environment based on predicted trajectories of one or more separate dynamic elements through the environment. The system identifies contextual cues associated with a monitored dynamic element, based on features of the dynamic element and actions of the dynamic element relative to various elements of the environment, including motions relative to other dynamic elements. A monitored dynamic element can be associated with a particular intention, which specifies a prediction of dynamic element movement through the environment, based on a correlation between identified contextual cues associated with the monitored dynamic element and a set of contextual cues which are associated with the particular intention. A predicted trajectory of the dynamic element is generated based on an associated intention. A targeted signal can be directed to a target dynamic element based on a predicted trajectory of the dynamic element.