On-Demand Vehicle Selection Using Risk Regression
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Solution Overview
Problem
The widespread adoption of autonomous vehicles 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 extensive logged mileage.
Innovation Solution
An on-demand transportation management system that utilizes risk regression and trip classification techniques to dynamically assess and manage autonomous vehicle operations, integrating human-driven, safety-driven autonomous, and fully autonomous vehicles, with a software verification process through simulation and real-world testing to ensure safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles undergo extensive real-world testing to establish safety records, then safety reliability improves, but deployment time and operational scalability worsen
Solution Approach 1:
The system performs preliminary software verification through simulation environments before real-world deployment. Software is tested in virtual scenarios to establish safety credentials in advance, allowing faster real-world deployment without sacrificing safety validation. This preliminary testing approach resolves the contradiction by preparing vehicles ahead of time with verified software.
Solution Approach 2:
The patent uses simulation environments that create virtual copies of real-world driving scenarios. These digital twins allow extensive safety testing without physical vehicle deployment, establishing safety records through simulated mileage that can be transferred to real-world operations, thereby reducing actual deployment time while maintaining reliability standards.
2Reliability
If autonomous vehicles accumulate extensive logged mileage for safety verification, then safety confidence improves, but time to market and operational readiness worsen
Solution Approach 1:
The system creates virtual copies of driving scenarios through simulation environments. Vehicles accumulate simulated mileage in these digital environments, generating safety verification data without physical deployment. This copying approach builds safety confidence through extensive virtual logging while maintaining high operational readiness since virtual testing occurs parallel to deployment preparations.
Solution Approach 2:
Extensive mileage accumulation and safety verification are performed preliminarily in simulation environments before real-world operations begin. This preliminary action establishes safety confidence in advance, allowing vehicles to enter service with pre-verified software and accumulated virtual mileage, thereby improving operational readiness without sacrificing safety confidence.
3Adaptability or versatility
If the system integrates multiple vehicle types (human-driven, safety-driven autonomous, fully autonomous), then service coverage and adaptability improve, but system complexity increases
Solution Approach 1:
The on-demand transportation management system is designed as a universal platform that handles multiple vehicle types through a unified interface. The same risk regression and trip classification algorithms apply to human-driven, safety-driven autonomous, and fully autonomous vehicles, creating a multi-functional system that achieves broad service coverage without proportionally increasing complexity through standardization.
Solution Approach 2:
The system manages different vehicle types by adjusting parameters within a unified framework. Vehicle characteristics such as autonomy level, safety driver presence, and operational constraints are represented as configurable parameters rather than fundamentally different system architectures. This parameter-based approach enables versatile service coverage while controlling system complexity through consistent management logic.
4Measurement precision
If the system performs dynamic risk analysis and trip classification for each request, then safety precision improves, but computational processing time worsens
Solution Approach 1:
The system performs preliminary risk regression analysis on trip routes before matching vehicles to requests. By pre-calculating risk metrics for potential trips, the system avoids performing complete risk assessments at the moment of matching, thereby maintaining high measurement precision while reducing processing time during actual vehicle-request pairing operations.
Solution Approach 2:
The risk assessment process is segmented into distinct phases: preliminary risk regression analysis of routes, followed by trip classification based on risk thresholds. This segmentation allows computationally intensive risk calculations to be performed in advance on divided route segments, improving both precision through detailed analysis and speed by distributing computations across multiple processing stages.
Data Source
AI summary
An on-demand transportation management service can perform a selection process between a set of safety-driven autonomous vehicles (SDAVs), fully autonomous vehicles (FAVs), and human-driven vehicles (HDVs) to service transport requests based on a variety of selection parameters.