Autonomous Vehicle Risk Assessment via Simulation Precertification
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Solution Overview
Problem
The widespread adoption of autonomous vehicles on public roads is hindered by the need for extensive real-world testing and a convincing safety record, as well as limitations in monetizing autonomy features, which require proven safety protocols and advanced technologies for risk assessment and vehicle management.
Innovation Solution
An on-demand transportation management system that utilizes risk regression and trip classification techniques to dynamically assess and manage risk, allowing for the integration of human-driven, safety-driven autonomous, and fully autonomous vehicles, with a software verification process that includes simulation-based precertification and real-world testing to ensure safety standards are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive real-world testing is conducted to establish safety records, then safety reliability is improved, but time consumption and testing costs increase
Solution Approach 1:
The patent implements simulation-based precertification that performs virtual safety testing before real-world deployment. Software is validated through comprehensive simulations of diverse driving scenarios, weather conditions, and edge cases in advance, allowing safety verification to occur prior to extensive real-world testing rather than requiring prolonged field testing to establish confidence
Solution Approach 2:
The patent creates virtual copies of real-world driving environments through high-fidelity simulations. These digital twins replicate road conditions, traffic patterns, weather, and sensor data to enable safety testing in virtual space, reducing the need for physical test miles while maintaining validation rigor
2Productivity
If dynamic risk assessment and trip classification systems are implemented, then vehicle deployment efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments the risk assessment system into distinct modular components: trip classification module that categorizes routes by risk level, real-time risk evaluation module that assesses current conditions, and software version verification module that checks capability matches. Each module operates independently with defined interfaces, allowing complex risk analysis to be distributed across specialized subsystems rather than a monolithic system
Solution Approach 2:
The patent dynamically adjusts operational parameters based on risk assessment results. Trip risk thresholds, software version requirements, and vehicle deployment decisions are modified according to real-time conditions and historical data. The system transitions between different operational states (e.g., restricted vs. full autonomy) based on quantified risk metrics, enabling adaptive deployment without hard-coded complexity
3Reliability
If software verification processes are made more rigorous with simulation-based precertification, then software reliability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs software verification through simulation-based precertification before real-world deployment. Software candidates undergo comprehensive virtual testing in diverse simulated environments including edge cases, adverse weather, and complex traffic scenarios. This preliminary validation establishes baseline safety metrics and identifies required software capabilities before field testing begins
Solution Approach 2:
The patent uses virtual copies of real-world driving scenarios to validate software. High-fidelity simulations replicate sensor data, road conditions, and traffic patterns without requiring physical test vehicles. This digital replication enables parallel execution of multiple test cases and accelerates verification throughput while maintaining rigor
Data Source
AI summary
An autonomous vehicle (AV) software management system can collect historical data of harmful events of human-driven vehicles (HDVs) within an autonomy grid on which AVs operate. For each path segment of the autonomy grid, the system can determine a fractional risk value for HDVs. The system may also receive AV data from a fleet of AVs operating throughout the autonomy grid, and for each path segment of the autonomy grid, the system can evaluate AV performance against the fractional risk values for HDVs based on the received AV data.