Risk-Bounded Control Barrier Functions for Vehicle Trajectory Safety
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Solution Overview
Problem
Current control barrier functions in autonomous vehicles heavily rely on martingale theory, leading to overestimation of unsafe outcomes and excessive degradation of vehicle performance in achieving planned trajectories.
Innovation Solution
Implementing risk-bounded control barrier functions (RB-CBF) that estimate vehicle states, plan trajectories, and apply risk-bounded control inputs using a quadratic program to ensure safe operation without significant performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If martingale theory is used for control barrier functions in stochastic settings, then safety constraints are enforced, but the fraction of unsafe outcomes is overestimated and vehicle performance degrades excessively
Solution Approach 1:
The patent transforms the control barrier function formulation by changing the mathematical parameters and structure from martingale-based to risk-bounded approach. Specifically, it introduces a new parameterization using value functions and risk bounds that fundamentally alters how safety constraints are expressed and enforced, enabling more accurate risk assessment without excessive performance degradation
Solution Approach 2:
The patent replaces the martingale theory mathematical framework with a risk-bounded control framework. This substitution involves replacing the stochastic process constraints of martingale theory with value function-based risk bounds, fundamentally changing the mathematical mechanism while maintaining safety enforcement capabilities
2Reliability
If control barrier functions apply substantial adjustments to control inputs, then safety is ensured, but the vehicle's ability to achieve planned trajectory is degraded
Solution Approach 1:
The patent applies partial action by introducing a risk bound parameter that allows the control system to apply only the necessary amount of correction to achieve safety, rather than excessive adjustments. The risk-bounded framework enables the system to apply control inputs proportionate to the actual risk level, maintaining trajectory accuracy while ensuring safety
3Measurement precision
If risk-bounded control barrier functions are implemented, then accurate risk bounding is achieved and trajectory performance is maintained, but computational complexity increases
Solution Approach 1:
The patent segments the control problem into distinct components: value function computation, risk bound calculation, and control input determination. This segmentation allows each component to be handled separately and efficiently, reducing overall computational complexity while maintaining accurate risk bounding capabilities
Data Source
AI summary
System, methods, and other embodiments described herein relate to implementing risk-bounded control barrier functions. In one embodiment, a method includes estimating a state of a vehicle; planning a trajectory based on the state of the vehicle; determining a nominal control input based on the trajectory; applying a risk-bounded control barrier function to determine a control input; and applying the control input to the vehicle.