AV Trajectory Prediction Training With Kinematics-Based Data Weighting
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Solution Overview
Problem
Existing autonomous vehicle (AV) prediction systems face challenges in accurately estimating the trajectories of encountered entities, leading to suboptimal navigation and routing decisions due to underperformance in machine-learning models.
Innovation Solution
Implement a kinematics model to compare and dynamically weight training instances where the AV's prediction layer underperforms, enhancing the training process by increasing the frequency and weight of these instances to improve prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning models are used for trajectory prediction, then the system can handle complex navigation tasks, but prediction accuracy deteriorates due to underperformance in challenging scenarios
Solution Approach 1:
A kinematics model is introduced as an intermediary to evaluate and compare with machine-learning predictions. The kinematics model provides a physics-based reference that identifies when ML predictions are inaccurate, enabling selective retraining on problematic scenarios without requiring the ML model to handle all scenarios equally well from the start
Solution Approach 2:
The training process dynamically adjusts the weight and frequency of training instances based on kinematics model evaluation. Scenarios where the kinematics model outperforms the ML model receive higher weights and increased training frequency, allowing the system to adaptively focus on improving accuracy in specific challenging scenarios rather than uniformly training on all data
2Reliability
If training data is uniformly weighted, then the training process is simple to implement, but prediction accuracy deteriorates in scenarios where the model underperforms
Solution Approach 1:
The training data weighting scheme transitions from static (uniform) to dynamic. The kinematics model continuously evaluates prediction performance and adjusts training instance weights in real-time based on performance metrics, allowing the system to automatically identify and focus on challenging scenarios without manual intervention
Solution Approach 2:
A feedback loop is established where the kinematics model evaluates ML predictions, identifies performance gaps, and feeds this information back into the training process. This feedback mechanism automatically adjusts training priorities, ensuring that the model receives targeted training on scenarios where it underperforms while maintaining computational efficiency
3Reliability
If all training instances are processed with equal frequency, then the training computation is balanced, but the model fails to improve in specific underperforming scenarios
Solution Approach 1:
The training approach transitions from uniform global processing to localized selective processing. Each training instance receives a different weight based on its specific performance characteristics evaluated by the kinematics model. Challenging scenarios receive enhanced local attention through increased weighting and retraining frequency, while well-performing scenarios maintain standard processing
Solution Approach 2:
Instead of uniformly processing all training instances, the system applies partial excessive action by repeatedly training on specific subsets of data where the kinematics model identifies performance deficiencies. This targeted retraining on problematic scenarios achieves better overall accuracy by focusing computational resources where they are most needed
Data Source
AI summary
The present disclosure generally relates to autonomous vehicle (AV) prediction systems and in particular, to novel methods for training a prediction layer of the AV software stack. In some aspects, the disclosure can provide a process for receiving road data representing a plurality of entities, predicting, using a prediction layer of an autonomous vehicle (AV), a first set of future trajectories for one or more of the plurality of entities, and predicting, using a kinematics model, a second set of future trajectories for one or more of the plurality of entities. In some approaches, the process can further include steps for flagging one or more instances in the road data for which the first set of future trajectories is less accurate than the second set of future trajectories. Systems and machine-readable media are also provided.