Bayesian Optimization for Autonomous Vehicle Controller Tuning
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Solution Overview
Problem
Existing methods for tuning control parameters of autonomous vehicle motion controllers rely heavily on human intuition or brute-force search methods, which are inefficient and computationally costly, especially in high-dimensional configuration spaces.
Innovation Solution
The use of Bayesian Global Optimization combined with Gaussian Process Regression (GPR) to iteratively determine optimal controller parameters by simulating configurations, generating scores, and refining the sampling process to minimize computational cost and maximize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human observers tune control parameters based on experience and intuition, then the process is simple and fast, but optimization of control parameters is not guaranteed
Solution Approach 1:
The system performs self-tuning of control parameters through automated algorithms (genetic algorithms, particle swarm optimization, gradient descent) that evaluate performance metrics and iteratively optimize parameters without human intervention, ensuring reliable optimization while reducing dependency on human expertise
Solution Approach 2:
The tuning process incorporates feedback loops where performance metrics are continuously evaluated based on simulated or real vehicle behavior, and parameter adjustments are made iteratively based on this feedback to guarantee optimization convergence to optimal or near-optimal solutions
2Reliability
If brute-force search using grid-search algorithms is used to tune parameters, then systematic exploration is achieved, but computational cost increases exponentially in high-dimensional spaces
Solution Approach 1:
The system transforms the high-dimensional parameter optimization problem into a lower-dimensional search space by using surrogate models that approximate the objective function, allowing efficient exploration with fewer evaluations while maintaining systematic coverage of the parameter space
Solution Approach 2:
Surrogate models (Gaussian processes, neural networks, random forests) are introduced as intermediaries between the actual expensive simulations and the optimization algorithm, enabling systematic parameter exploration at reduced computational cost by predicting performance without full simulations
3Manufacturing precision
If more parameters are tuned to improve controller performance, then control accuracy increases, but the configuration space dimension increases leading to exponential computational cost
Solution Approach 1:
The system performs dimensionality reduction by identifying and focusing on the most influential parameters through sensitivity analysis and surrogate model variable importance metrics, tuning only the critical subset of parameters to achieve desired control accuracy while avoiding the exponential cost of full parameter space exploration
Solution Approach 2:
The parameter tuning process is segmented into multiple stages: initial coarse exploration to identify promising regions, followed by fine-tuning of specific critical parameters, allowing systematic handling of high-dimensional spaces by breaking down the optimization into manageable segments
Data Source
AI summary
In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.