Wheel Direction Classification for Trajectory Prediction
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the trajectories of wheeled vehicles, especially when they are stopped or moving slowly, leading to conservative decision-making due to uncertainty, which can impact safety and efficiency.
Innovation Solution
The use of a machine-learned model trained to classify or regress the direction of wheels from a single image, combining this information with other data to estimate the trajectory of the wheeled vehicle, allowing for quicker and more accurate trajectory determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional trajectory prediction methods are used for stopped or slowly moving wheeled vehicles, then processing time is reduced, but measurement precision and reliability of trajectory prediction deteriorate
Solution Approach 1:
The system performs preliminary classification of wheel direction (straight, left, right) using a machine-learned model before full trajectory prediction. This preliminary action on wheel direction classification enables faster processing while maintaining accurate trajectory prediction by providing prior information that constrains the prediction space.
2Reliability
If conservative decision-making is adopted due to trajectory prediction uncertainty, then safety is improved, but productivity and efficiency deteriorate
Solution Approach 1:
The system uses feedback from wheel direction classification to continuously refine trajectory predictions. By monitoring wheel direction changes and updating trajectory estimates in real-time, the system maintains high reliability for safety-critical decisions while improving efficiency through more confident, less conservative planning when predictions are accurate.
3Measurement precision
If complex trajectory prediction algorithms are used, then measurement precision improves, but device complexity increases
Solution Approach 1:
The trajectory prediction system is segmented into multiple components: wheel direction detection, machine-learned classification, and trajectory estimation. This segmentation allows each component to specialize in a specific task, improving overall measurement precision while managing device complexity through modular architecture.
Solution Approach 2:
Wheel direction classification serves as an intermediary between raw sensor data and final trajectory prediction. This intermediate step simplifies the overall algorithm by breaking down the complex prediction task into manageable stages, improving precision without proportionally increasing complexity.
Data Source
AI summary
A trajectory estimate of a wheeled vehicle can be determined based at least in part on determining a wheel angle associated with the vehicle. In some examples, at least a portion of the image associated with the wheeled vehicle may be input into a machine-learned model that is trained to classify and/or regress wheel directions of wheeled vehicles. The machine-learned model may output a predicted wheel direction. The wheel direction and/or additional or historical sensor data may be used to estimate a trajectory of the wheeled vehicle. The predicted trajectory of the object can then be used to generate and refine an autonomous vehicle's trajectory as the autonomous vehicle proceeds through the environment.