Reinforcement Learning Scene Decomposition Navigation
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Solution Overview
Problem
Current navigation systems for vehicles face challenges in efficiently and reliably navigating complex urban environments, such as road intersections with multiple cars and pedestrians, due to difficulties in scaling decision-making algorithms and handling sensor uncertainty.
Innovation Solution
A method involving reinforcement learning (RL) with scene decomposition, where observation data from sensors is used to generate projection data and predict interactions between the vehicle and traffic participants, determining safe actions to reach a target location, incorporating a policy component with a projection data updater, prediction component, and scene decomposition component to handle occlusions and uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning with scene decomposition is implemented, then navigation reliability and robustness in complex environments is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by decomposing the complex driving scene into multiple independent sub-scenes or regions of interest. This allows the reinforcement learning system to process each sub-scene separately, reducing the overall computational complexity while maintaining navigation reliability in complex environments with multiple road users.
Solution Approach 2:
The patent introduces a temporal dimension by generating projection data for multiple future time steps. This allows the system to predict interactions ahead of time, improving navigation reliability by anticipating potential hazards while distributing computational load across time rather than requiring all calculations to be performed simultaneously.
2Measurement precision
If projection data is generated for multiple time steps, then prediction accuracy of interactions is improved, but computational load and processing time increase
Solution Approach 1:
The patent applies preliminary action by generating projection data for multiple future time steps in advance. This allows the system to predict interactions before they occur, improving prediction accuracy while enabling early decision-making. The computational load is distributed over time, reducing the critical processing time required at each decision moment.
3Reliability
If scene decomposition is used to handle occlusions, then robustness to sensor uncertainty is improved, but algorithm complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the occluded scene into visible and occluded regions, processing each separately. This allows the system to handle sensor uncertainty in occluded areas using different strategies (e.g., prediction models) while maintaining simple processing for visible areas, improving robustness without uniformly increasing algorithm complexity across the entire scene.
Solution Approach 2:
The patent introduces projection data as an intermediary representation that bridges the gap between sensor observations and navigation decisions. This intermediary layer handles the complexity of dealing with occlusions and sensor uncertainty, allowing the core navigation algorithm to work with simplified, predicted information rather than raw, uncertain sensor data.
4Reliability
If interaction prediction for multiple traffic participants is performed, then navigation safety is improved, but computational resources required increase
Solution Approach 1:
The patent applies segmentation by processing each traffic participant and their interactions separately through scene decomposition. This allows the system to focus computational resources on critical interactions while using simplified models for less critical participants, improving navigation safety through comprehensive interaction prediction while managing computational resource consumption.
Data Source
AI summary
Systems and methods for providing navigation to a vehicle may include receiving observation data from one or more sensors of the vehicle, generating projection data corresponding to the one or more traffic participants based on the observation data for each time step within a time period, and predicting interactions between the vehicle, the one or more traffic participants, and the one or more obstacles, based on the projection data of the one or more traffic participants. The systems and methods may further include determining a set of actions by the vehicle corresponding to a probability of the vehicle safely arriving at a target location based on the predicted interactions, and selecting one or more actions from the set of actions and provide the one or more actions to a navigation system of the vehicle, wherein the navigation system uses the navigation data to provide navigation instructions to the vehicle.