Variable Motion Control Envelope for Road-Adaptive Autonomous Driving
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Solution Overview
Problem
Self-driving vehicles face challenges in maintaining control due to friction interactions with road surfaces, actuation forces, and external factors like road grade and wind gusts, which affect driving operations and path planning.
Innovation Solution
A variable motion control envelope system that estimates future vehicle behavior by evaluating intrinsic vehicle properties and extrinsic environmental influences, generating an available acceleration model to dynamically adjust driving operations, using sensors and processors to manage longitudinal and lateral accelerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the vehicle operates in autonomous driving mode with fixed motion control limits, then the control system is simple to implement, but the vehicle cannot adapt to changing road conditions and external factors
Solution Approach 1:
The motion control envelope is transformed from a static fixed limit to a dynamic variable envelope that adapts in real-time. The system continuously estimates friction envelopes, external forces, and internal forces to recalculate acceleration limits along the path, allowing the vehicle to adjust its motion control boundaries dynamically based on current road conditions and environmental factors
Solution Approach 2:
The system implements continuous feedback by monitoring tire state data, road surface information, and vehicle sensor data to estimate friction envelopes and external forces. This feedback loop enables the control system to update the motion control envelope based on actual vehicle behavior and changing conditions, improving adaptability while managing complexity through systematic feedback processing
2Reliability
If the vehicle uses a variable motion control envelope that estimates future vehicle behavior, then the vehicle maintains desired control under varying conditions, but the computational complexity increases
Solution Approach 1:
The system performs preliminary estimation of friction envelopes, external forces, and internal forces before executing motion control decisions. By pre-calculating the variable motion control envelope based on predicted vehicle behavior and upcoming path conditions, the system ensures control stability is maintained while managing computational complexity through advance preparation
Solution Approach 2:
The motion control problem is segmented into discrete components: friction envelope estimation, external force estimation, internal force estimation, and envelope generation. Each component is processed separately along path segments, allowing the system to manage computational complexity by breaking down the overall problem into manageable segments that can be solved independently
3Measurement precision
If the vehicle evaluates multiple factors including friction, external forces, and internal forces, then the available acceleration model is accurate, but the processing time increases
Solution Approach 1:
The system applies local quality by estimating friction envelopes, external forces, and internal forces specifically for relevant path segments rather than uniformly across the entire operating range. This localized estimation approach maintains high accuracy for the current driving context while reducing processing time by focusing computational resources on locally relevant factors
Data Source
AI summary
The technology employs a variable motion control envelope that enables an on-board computing system of a self-driving vehicle to estimate future vehicle driving behavior along an upcoming path, in order to maintain a desired amount of control during autonomous driving. Factors including intrinsic vehicle properties, extrinsic environmental influences and road friction information are evaluated. Such factors can be evaluated to derive an available acceleration model, which defines an envelope of maximum longitudinal and lateral accelerations for the vehicle. This model, which may identify dynamically varying acceleration limits that can be affected by road conditions and road configurations, may be used by the on-board control system (e.g., a planner module of the processing system) to control driving operations of the vehicle in an autonomous driving mode.