Behavior Prediction Feature Vectors for Interpretable Autonomous Driving
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Solution Overview
Problem
End-to-end autonomous driving systems face challenges in predicting behaviors based on original sensor data, leading to cumulative errors and a lack of interpretability, requiring high-quality training data and struggling with the 'black box' nature of the prediction process.
Innovation Solution
A content generation method that obtains first visual data and control data to generate feature vectors, allowing for the prediction of target object behaviors in autonomous driving scenarios, ensuring predictions conform to physical laws and providing interpretability through the use of a diffusion transformer and additional dynamic object control data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If end-to-end autonomous driving systems use original sensor data for behavior prediction, then the system can operate with a simplified architecture, but cumulative errors occur and interpretability is lost
Solution Approach 1:
The patent segments the autonomous driving system into distinct modules: a diffusion model for generating diverse trajectories and a transformer model for selecting the most likely behavior. This segmentation allows each module to specialize in specific tasks, reducing cumulative errors while maintaining architectural simplicity.
Solution Approach 2:
The patent introduces intermediate representations (feature vectors, trajectory embeddings) as mediators between sensor data and behavior predictions. These intermediates provide structured information that improves prediction reliability while maintaining system interpretability through controlled transformation steps.
2Device complexity
If end-to-end autonomous driving systems use original sensor data for behavior prediction, then the system architecture can be simplified, but the prediction process becomes a black box without interpretability
Solution Approach 1:
The patent implements feedback mechanisms where the transformer model evaluates diffusion-generated trajectories and provides guidance back to the diffusion process. This iterative feedback loop maintains interpretability by making the prediction process transparent and controllable while preserving architectural simplicity.
Solution Approach 2:
The patent introduces intermediate representations (feature vectors, trajectory embeddings) as mediators between sensor data and behavior predictions. These intermediates provide structured information that improves prediction reliability while maintaining system interpretability through controlled transformation steps.
3Reliability
If high-quality training data is used to improve prediction accuracy, then behavior prediction reliability increases, but the system still suffers from cumulative errors and lack of interpretability
Solution Approach 1:
The patent implements feedback mechanisms where the transformer model evaluates diffusion-generated trajectories and provides guidance back to the diffusion process. This iterative feedback loop maintains interpretability by making the prediction process transparent and controllable while preserving architectural simplicity.
Solution Approach 2:
The patent changes the parameters of the prediction process by using diffusion models to generate diverse trajectories and transformers to select the most likely behavior. This parameter transformation approach improves reliability while maintaining interpretability through controlled probabilistic modeling.
Data Source
AI summary
A computer-implemented method for content generation is provided. The method includes obtaining first visual data at a specific moment and first control data for controlling content generation, the first visual data including information associated with an environment where a target object is located at the specific moment. The method further includes generating a first feature vector associated with the first visual data. The method further includes generating, based on the first feature vector, a second feature vector under the control of the first control data, the second feature vector including information that characterizes a behavior of the target object in the environment at a subsequent moment after the specific moment.