Top-Down Trajectory Prediction for Complex Autonomous Driving Scenes
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Solution Overview
Problem
Current prediction techniques for determining future states of entities in an environment, such as autonomous vehicles, often rely on physics-based modeling or rules-of-the-road simulations, which may not accurately predict the behavior of agents in complex scenarios.
Innovation Solution
The system captures sensor data from an autonomous vehicle to generate a top-down representation of the environment, including semantic information about agents and the environment. This data is input into a prediction system, which uses machine learning models, such as convolutional neural networks, to output heat maps representing prediction probabilities. These probabilities are then used to generate predicted trajectories for agents in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physics-based modeling or rules-of-the-road simulations are used for prediction, then the system can predict future states of entities, but the prediction accuracy in complex scenarios deteriorates
Solution Approach 1:
The patent replaces physics-based modeling and rules-of-the-road simulations with machine learning models that learn patterns directly from data. The system uses neural networks to predict future states of entities, substituting mechanical prediction methods with data-driven approaches that better handle complex, real-world scenarios.
Solution Approach 2:
The patent transforms the prediction approach by changing from fixed physics-based parameters to learned parameters from training data. The machine learning models adapt their parameters based on observed entity behaviors, enabling accurate predictions in complex scenarios where traditional physics models fail.
2Reliability
If machine learning models are used for prediction, then prediction accuracy in complex scenarios improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline using extensive training data. The models learn entity behavior patterns beforehand, so during runtime, predictions can be made quickly without complex real-time computations. This separates the heavy computational work (training) from the operational phase.
Solution Approach 2:
The patent uses copying by creating simplified representations of entity behaviors through trained model parameters. Once trained, the model copies learned patterns into a compact form that can be rapidly applied for predictions without reprocessing the entire training dataset, reducing computational complexity during operation.
Data Source
AI summary
Techniques are discussed for determining prediction probabilities of an object based on a top-down representation of an environment. Data representing objects in an environment can be captured. Aspects of the environment can be represented as map data. A multi-channel image representing a top-down view of object(s) in the environment can be generated based on the data representing the objects and map data. The multi-channel image can be used to train a machine learned model by minimizing an error between predictions from the machine learned model and a captured trajectory associated with the object. Once trained, the machine learned model can be used to generate prediction probabilities of objects in an environment, and the vehicle can be controlled based on such prediction probabilities.