Autonomous Vehicle Motion Planner Hyperparameter Tuning for Generalization
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Solution Overview
Problem
Current autonomous vehicle motion planners face challenges in selecting optimal hyperparameters, as the relationship between hyperparameters and motion output is complex, making it difficult to determine the best set for accurate and safe decisions, especially in varying environments.
Innovation Solution
An apparatus and method using Bayesian optimization and evolutionary algorithms to generate improved hyperparameters by building a model from existing data, calculating new utility scores based on truth outcome data, and iteratively refining trial hyperparameters to outperform current settings, ensuring generalizability to new scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion planners use many hundreds or thousands of hyperparameters to achieve accurate decisions, then decision accuracy is improved, but the complexity of selecting and tuning these hyperparameters increases significantly
Solution Approach 1:
The system employs automated hyperparameter optimization algorithms that enable the motion planner to self-tune its hyperparameters without extensive manual intervention. The algorithms automatically search the hyperparameter space, evaluate performance, and identify optimal configurations, allowing the system to serve itself in the parameter tuning process.
Solution Approach 2:
The invention systematically varies hyperparameter values across different dimensions (learning rates, regularization parameters, network architecture parameters) to explore the parameter space. By methodically changing parameters and evaluating their impact on decision accuracy, the system identifies optimal configurations that balance accuracy with computational efficiency.
2Reliability
If motion planners are trained extensively to achieve accurate decisions in known scenarios, then performance in training scenarios is improved, but the ability to generalize to new locations deteriorates due to overfitting
Solution Approach 1:
The system applies regularization techniques that intentionally constrain the model's capacity to fit training data perfectly. By adding penalty terms to the loss function and controlling model complexity, the system prevents excessive fitting to training scenarios, thereby maintaining better generalization capabilities to unseen locations and conditions.
Solution Approach 2:
The hyperparameter optimization process incorporates feedback from performance evaluations on both training and validation datasets. By monitoring performance metrics across different hyperparameter configurations and using this feedback to guide the search process, the system identifies configurations that achieve good training performance while maintaining generalization ability.
3Measurement precision
If computational resources are increased to repeatedly run motion planner algorithms during training, then hyperparameter optimization accuracy is improved, but computational cost and time consumption increase
Solution Approach 1:
The system performs preliminary evaluations of hyperparameter configurations using surrogate models or approximations before committing to full-scale training runs. By pre-screening promising configurations and eliminating poor ones early in the optimization process, the system reduces the number of expensive computational experiments needed to achieve optimal hyperparameters.
Solution Approach 2:
The hyperparameter optimization process maintains continuous learning and adaptation throughout the training process. By iteratively refining hyperparameter estimates based on accumulating performance data and using this information to guide subsequent training iterations, the system achieves efficient convergence to optimal configurations without requiring exhaustive computational resources.
Data Source
AI summary
An apparatus is provided for determining improved motion planner hyperparameters. The apparatus is configured to: receive a data pair comprising a set of hyperparameters and a utility score defining a utility of a motion planner outcome resulting from the set of hyperparameters; provide a model defining a relationship between the hyperparameters and the utility score; generate trial hyperparameters using a guidance objective configured to evaluate a quality of trial sets of hyperparameters in dependence on the model; determine a trial outcome of the motion planner based on the trial hyperparameters; and determine a new utility score of the trial hyperparameters based on comparing the trial outcome with truth data. The apparatus therefore optimises currently deployed hyperparameters by comparison with truth data in order to provide hyperparameters that can produce realistic motion planning trajectories and outperform currently deployed hyperparameters.