Autonomous Vehicle Behavior Estimation for Off-Road Stability Control
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Solution Overview
Problem
Conventional heavy equipment and vehicles face challenges in dynamic stability control due to unstable or variable working surfaces in diverse and unstructured work-site environments, such as construction and mining sites, where it is difficult to distinguish different surfaces like paved, dirt, grass, or gravel, leading to poor control of autonomous operations.
Innovation Solution
An autonomous vehicle management system that uses predictive modeling to estimate kinematic and dynamic behavior, processing sensor data to generate internal maps and control vehicle systems like steering, braking, and propulsion, employing AI techniques like Gaussian processes and reinforcement learning to stabilize operations and improve task performance in off-road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional heavy equipment operates in diverse off-road conditions, then the equipment can perform tasks in various work-site environments, but the dynamic stability control deteriorates due to unstable or variable working surfaces
Solution Approach 1:
The system dynamically adapts to changing working surfaces by continuously estimating kinematic and dynamic behavior based on real-time sensor data. The predictive model updates control parameters on-the-fly, allowing the equipment to maintain stability across diverse and variable off-road conditions rather than relying on fixed control parameters designed for specific surfaces.
2Reliability
If predictive modeling is used to estimate kinematic and dynamic behavior, then control accuracy improves in diverse conditions, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical control systems with software-based predictive modeling and estimation algorithms. By using Gaussian processes and reinforcement learning to model kinematic and dynamic behavior, the system achieves high control accuracy through computational methods rather than complex mechanical mechanisms, thereby reducing physical system complexity while improving reliability.
3Reliability
If real-time sensor data is processed continuously to update predictive models, then operational stability improves, but computational energy consumption increases
Solution Approach 1:
The system maintains continuous processing of sensor data to update predictive models in real-time, ensuring operational stability throughout the task execution. This continuous action allows the equipment to adapt to changing conditions without interruption, maintaining stability while managing computational energy through efficient online learning algorithms that build upon previous model states rather than recalculating from scratch.
Data Source
AI summary
The present disclosure relates generally to techniques for the kinematic estimation and dynamic behavior estimation of autonomous heavy equipment or vehicles to improve navigation, digging and material carrying tasks at various industrial work sites. Particularly, aspects of the present disclosure are directed to obtaining a set of sensor data providing a representation of operation of an autonomous vehicle in a worksite environment, estimating, by a trained model comprising a Gaussian process, a set of output data based on the set of sensor data, controlling an operation of the autonomous vehicle in the worksite environment using input data derived from the set of sensor data and the set of output data, obtaining actual output data from the operation of the autonomous vehicle in the worksite environment, and updating the trained model with the input data and the actual output data.