Dynamic Tolerance Adjustment for Autonomous Vehicle Motion Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in maintaining precise control while navigating diverse environments, as existing systems lack the ability to dynamically adjust precision levels based on context, leading to suboptimal performance in terms of safety and comfort.
Innovation Solution
A vehicle controller system that determines tolerance values based on current context, allowing for adjustable precision in motion control by generating actuator commands to keep the vehicle within defined longitudinal and lateral deviations, thereby enhancing navigation in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed precision control is used for autonomous vehicle motion control, then control reliability is maintained in critical situations, but control flexibility and adaptability deteriorate across diverse environments
Solution Approach 1:
The system dynamically adjusts motion control tolerance values based on real-time context assessment. The controller evaluates current driving conditions (e.g., urban environment, highway, weather, traffic density) and adapts the precision requirements accordingly, transitioning from fixed to dynamic control parameters that optimize both reliability and adaptability across varying operational contexts.
Solution Approach 2:
The system changes the tolerance parameter values based on assessed context conditions. When high safety requirements are detected (e.g., pedestrian zones, intersections), the system tightens motion control tolerance values; when conditions are less critical, it relaxes tolerance values to improve ride comfort and reduce unnecessary actuator adjustments, thereby optimizing control parameters for each specific situation.
2Reliability
If high precision control is applied in all situations, then safety is improved, but ride comfort and system responsiveness deteriorate in less demanding scenarios
Solution Approach 1:
The system applies different precision levels to different spatial and contextual conditions. In high-risk zones (e.g., near pedestrians, intersections, urban environments), strict precision control is enforced; in low-risk zones (e.g., open highways, controlled access roads), relaxed precision control is applied. This localized quality approach ensures safety where needed while maintaining comfort where permissible.
Solution Approach 2:
The controller dynamically modifies motion tolerance parameters based on real-time context assessment. When assessing high-safety requirements, the system reduces tolerance values for longitudinal and lateral deviations; when assessing lower-risk conditions, it increases tolerance values, thereby adjusting control stringency to match actual safety needs and improving overall ride comfort.
3Adaptability or versatility
If dynamic tolerance adjustment is implemented, then adaptability and ride comfort are improved, but control complexity and computational requirements increase
Solution Approach 1:
The context assessment system is segmented into distinct evaluation modules that independently assess different aspects of the driving environment (e.g., road type, weather conditions, traffic density, proximity to vulnerable road users). Each module generates specific context indicators that feed into the tolerance adjustment logic, breaking down the complex decision-making process into manageable, modular components that reduce overall system complexity.
Data Source
AI summary
The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.