On-Demand Fleet Utilization for Autonomous Vehicle Safety
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Solution Overview
Problem
The widespread adoption of autonomous vehicles on public roads is hindered by the need for a proven safety track record and extensive real-world testing, as existing technologies lack comprehensive methods for dynamic risk assessment and robust software verification to ensure safe operation across varying conditions.
Innovation Solution
An on-demand transportation management system that utilizes risk regression and trip classification techniques, combined with simulation-based software verification, to dynamically assess and mitigate risks, ensuring the safe operation of autonomous vehicles by selecting the most optimal vehicle type and software version for each route, and enabling dynamic software switching and post-trip management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles undergo extensive real-world testing to build a proven safety track record, then safety reliability improves, but deployment time and loss of time increase
Solution Approach 1:
The patent applies preliminary action by conducting simulation-based software verification before real-world deployment. The system performs extensive safety testing and risk assessment in virtual environments, allowing autonomous vehicle software to be validated and verified prior to actual road testing. This preliminary verification reduces the need for extensive real-world testing while maintaining safety standards.
Solution Approach 2:
The patent uses copying by creating virtual replicas of real-world driving scenarios through simulation environments. Instead of physically testing every possible situation, the system copies and recreates diverse driving conditions, road types, weather conditions, and traffic scenarios in simulation, enabling comprehensive safety verification without the time costs of actual physical testing.
2Reliability
If dynamic risk assessment and software verification systems are implemented, then safety reliability improves, but device complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional verification platform that handles multiple tasks: software validation, risk assessment, scenario simulation, and safety verification. This single integrated system performs what would otherwise require multiple separate systems, reducing overall complexity while maintaining comprehensive safety verification capabilities across diverse driving conditions.
Solution Approach 2:
The system applies self-service by enabling autonomous vehicles to self-verify their software and self-assess risks before deployment. The simulation-based verification framework allows the vehicle's own software to be tested and validated without requiring external testing infrastructure, reducing system complexity while maintaining rigorous safety standards.
3Adaptability or versatility
If comprehensive risk analysis frameworks are deployed across diverse conditions, then adaptability improves, but computational energy use increases
Solution Approach 1:
The patent applies preliminary action by pre-computing risk assessments and verification results during the simulation phase before actual deployment. By performing comprehensive risk analysis in advance during virtual testing, the system reduces the computational energy required during actual vehicle operation, as much of the heavy computational work has already been completed during offline simulation and verification.
Solution Approach 2:
The patent substitutes computational mechanics by replacing extensive physical testing with simulation-based verification. Instead of physically testing every scenario which would require enormous energy and time resources, the system uses computational simulations to model and analyze diverse driving conditions, achieving comprehensive adaptability verification with reduced energy consumption compared to physical experimentation.
Data Source
AI summary
An on-demand transportation management system can collect vehicle fleet utilization data corresponding to human-driven vehicles (HDVs) and autonomous vehicles (AVs) operating within a given region in connection with an on-demand transportation service. The on-demand transportation management system can then establish a set of selection priorities for respective areas of the given region based on the vehicle fleet utilization data, each selection priority indicating whether a respective area of the given region is to favor HDVs or AVs for servicing transport requests.


