Auto-Regressive Vehicle Control Distributions for Complex Navigation
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Solution Overview
Problem
Existing vehicle automation systems struggle to handle the complexity of real-world scenarios, particularly in dense urban environments or extreme weather conditions, while maintaining real-time computing capabilities.
Innovation Solution
A novel machine-learned pipeline that auto-regressively generates controls for vehicles by producing distributions of steering angle and velocity, rather than discrete controls, allowing for multiple possible trajectories and enhancing the vehicle's ability to navigate complex situations autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing vehicle automation systems use discrete controls for navigation, then the control logic is simple, but the system cannot adequately handle complex real-world scenarios in dense urban environments or extreme weather conditions
Solution Approach 1:
The patent replaces traditional discrete control logic with a continuous control distribution generation system based on world state embeddings. Machine learning models generate probability distributions over continuous control spaces (steering angle, velocity, acceleration) rather than discrete control decisions, enabling the system to handle complex scenarios while maintaining real-time performance through efficient embedding representations.
Solution Approach 2:
The system transforms the control output from discrete values to continuous probability distributions parameterized by mean and standard deviation. This parameterization allows the system to represent uncertainty and multiple possible trajectories simultaneously, improving adaptability to complex scenarios while the efficient computation of distribution parameters maintains real-time capability.
2Adaptability or versatility
If the system generates multiple possible trajectories using control distributions, then the vehicle's ability to navigate complex situations improves, but the computational complexity increases
Solution Approach 1:
The patent extracts the essential features of the world state into compact embedding representations that capture the critical information needed for control decision-making. By separating the complex sensor data processing from the control generation and using efficient embedding layers, the system reduces computational complexity while maintaining the ability to generate multiple plausible trajectories through distribution-based outputs.
Solution Approach 2:
The system generates control distributions that represent multiple possible trajectories simultaneously rather than computing all possible paths. By using probability distributions parameterized by mean and standard deviation, the system captures the essence of multiple trajectories without the full computational burden of enumerating and evaluating each individual path, achieving partial action that is sufficient for safe navigation.
3Extent of automation
If the system uses auto-regressive control distribution generation, then the vehicle can exhibit complex behavior autonomously, but the computational time for real-time decisions increases
Solution Approach 1:
The system pre-computes world state embeddings from sensor data and pre-trains control distribution models offline. During real-time operation, the pre-trained models directly generate control distributions from the current world state embedding without requiring complex on-the-fly computations. This preliminary preparation enables the system to exhibit complex autonomous behavior while making real-time decisions efficiently.
Solution Approach 2:
The auto-regressive nature of the control distribution generation allows the system to continuously update control predictions based on the current world state and previous control distributions. This continuous generation of control actions without interruption maintains autonomous navigation capability while the efficient embedding-based approach ensures that each control decision is computed quickly enough for real-time operation.
Data Source
AI summary
A machine-learned architecture for generating a single trajectory or multiple trajectories for controlling a vehicle may comprise an embedding model that generates an embedding of a world state indicating environment, object, and/or other states, one or more machine-learned layers that determine a predicted world state embedding using the world state embedding, and concatenating, as combined data, that predicted world state embedding to a steering angle distribution and a velocity distribution. The combined data is provided as input to a machine-learned model (that may be a single machine-learned model or may comprise two separate machine-learned models) that determines a next steering angle distribution and a next velocity distribution. These distributions may be used as part of generating one or more trajectories for controlling the vehicle. The architecture may be iteratively used to create a series of distributions that are used to create one or more trajectories.