Modular Goal Prediction for Out-of-Distribution Vehicle Control
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Solution Overview
Problem
Existing autonomous vehicle control systems relying on machine learning models face limitations in generalizing to novel scenarios due to exclusive imitation from expert demonstrations, leading to spurious actions in out-of-distribution observations and misidentification of causal structures.
Innovation Solution
The systems and methods decompose vehicle operating tasks into modular skill primitives, utilizing a modular pipeline that includes an obstacle-awareness module, distribution-aware goal prediction module, conformant model-based planning, and trajectory pruning to generate diverse multi-modal predictions grounded on vehicle kinematics and road geometry, leveraging expert demonstrations for pre-training and density estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models rely exclusively on expert demonstrations for training, then the models can learn from available data, but they fail to generalize to novel scenarios and produce spurious actions in out-of-distribution observations
Solution Approach 1:
The patent segments the vehicle control task into modular skill primitives (e.g., lane changing, merging, turning) that can be independently learned and combined. This segmentation allows the system to generalize to novel scenarios by composing known primitives rather than relying on exhaustive training data for every possible situation, directly addressing the generalizability issue while maintaining reliability through structured decision-making.
Solution Approach 2:
The patent introduces goal distribution predictions as an intermediary between expert demonstrations and vehicle control decisions. Instead of directly mapping observations to actions, the system first predicts goal distributions that capture the uncertainty and diversity of possible outcomes. This intermediary layer enables better generalization to out-of-distribution scenarios by providing a probabilistic bridge that handles novel situations more robustly.
2Loss of information
If machine learning models use large training data sets from expert demonstrations, then they can capture driving patterns, but they misidentify causal structures and produce spurious actions
Solution Approach 1:
The patent replaces the purely data-driven mechanical learning process with a hybrid approach that incorporates physics-based vehicle dynamics models and kinematic constraints. By substituting some of the pattern recognition functionality with explicit physical models, the system captures driving patterns more accurately while avoiding spurious causal inferences that pure machine learning might produce from correlated but causally unrelated features.
3Adaptability or versatility
If the system generates diverse multi-modal predictions, then it improves generalizability, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent segments the prediction process into separate modules for different skill primitives, each generating predictions for specific maneuver types. This segmentation allows diverse multi-modal predictions to be generated in a computationally efficient manner by parallelizing primitive-specific processing rather than requiring a single complex model to handle all possibilities, thus reducing overall computational complexity while maintaining prediction diversity.
Solution Approach 2:
The patent generates goal distribution predictions that focus on the most relevant and probable goals rather than exhaustively predicting all possible future states. By performing partial action (predicting only the most significant goal distributions), the system achieves sufficient diversity for generalizability while avoiding the computational burden of generating excessively detailed multi-modal predictions for every possible outcome.
Data Source
AI summary
A method includes generating, using a machine learning model and at a first time interval, a first current vehicle position prediction and generating, using the machine learning model, at a second time interval, a first historical vehicle trajectory prediction based on at least the first current vehicle position prediction and previous spatial information. The method also includes generating, using the machine learning model, at a third time interval, a first future vehicle position prediction based on the first current vehicle position prediction and the first historical vehicle trajectory predication. The method also includes receiving, at the first time interval, sensor data and a sequence of waypoints, and controlling, at the first time interval, at least one vehicle operation of the vehicle using the first current vehicle position prediction, the first historical vehicle trajectory prediction, the first future vehicle position prediction, the sensor data, and the sequence of waypoints.