Autonomous Vehicle Hazard Probability Adaptation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately estimating hazard and risk compliance with safety standards due to the lack of empirical data, leading to imperfect estimates that may result in underperformance or overperformance, which can invalidate safety certifications and lead to inefficient driving behaviors.
Innovation Solution
A system that continuously evaluates the hazard and risk safety of autonomous vehicles using actual operational information to adapt driving parameters in real-time, ensuring compliance with safety standards by adjusting probabilities based on operational data from sensors and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical models are used to estimate hazard and risk compliance, then certification can be obtained without sufficient empirical data, but the estimates may be imperfect leading to underperformance or overperformance with respect to safety standards
Solution Approach 1:
The system continuously monitors actual operational data from the autonomous vehicle and compares it against the statistical model predictions. This feedback loop allows the system to detect deviations between estimated and actual hazard probabilities, enabling continuous refinement of the statistical models to improve accuracy over time while maintaining certification capability.
Solution Approach 2:
The system performs preliminary hazard and risk analysis using statistical models before the vehicle accumulates sufficient empirical data for certification. This preliminary action enables manufacturers to obtain safety certification based on simulated and estimated data, with the understanding that the models will be continuously refined as real operational data becomes available.
2Ease of manufacture
If statistical estimates are used for hazard probability, then certification can proceed with limited data, but the driving behavior may become inefficient due to underperformance or overperformance
Solution Approach 1:
The system dynamically adjusts driving parameters based on the comparison between statistical model predictions and actual operational data. As the vehicle accumulates real-world experience, the system adapts its hazard probability estimates and corresponding driving behaviors, transitioning from static statistical assumptions to dynamic, data-driven decision-making that optimizes both safety compliance and driving efficiency.
Solution Approach 2:
The system changes key parameters including hazard probability estimates, risk thresholds, and driving parameter adjustments based on the continuous comparison between statistical models and actual operational data. This parameter adaptation allows the vehicle to optimize its driving behavior as it accumulates empirical evidence, improving both certification validity and operational efficiency.
3Measurement precision
If continuous monitoring of operational data is implemented, then safety compliance accuracy is improved, but system complexity increases
Solution Approach 1:
The system leverages existing sensors and data infrastructure already present in autonomous vehicles for other primary functions such as navigation, obstacle detection, and path planning. By repurposing this existing multi-functional data collection capability for hazard and risk analysis, the system achieves continuous monitoring without proportionally increasing system complexity.
Solution Approach 2:
The system merges the hazard and risk analysis functionality with the existing autonomous driving control systems and data processing pipelines. By integrating the statistical model comparisons and parameter adjustments into the existing decision-making architecture, the system achieves continuous safety monitoring while minimizing additional computational overhead and system complexity.
Data Source
AI summary
Disclosed herein are systems, devices, and methods for hazard and risk analysis systems that use operational information about an autonomous vehicle in order to continuously determine a hazard and risk analysis of a vehicle and its compliance with safety standards. The hazard and risk analysis system estimates a hazard probability for a vehicle based on operational information about the vehicle, wherein the hazard probability represents a likelihood that the vehicle will experience a driving event over a predefined interval. The hazard and risk analysis also adjusts a driving parameter of the vehicle based on the hazard probability if the hazard probability deviates from a predefined hazard safety criterion.