Route Risk Assessment for Semi-Autonomous Vehicle Navigation
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Solution Overview
Problem
Current systems lack an effective method to assess and mitigate risks associated with vehicles operating in autonomous or semi-autonomous modes, particularly in determining insurance-related costs and ensuring proper coverage, as they do not provide real-time risk assessment and adaptive route selection.
Innovation Solution
A computing system that calculates real-time risk values for road segments using accident, geographic, and vehicle information, allowing for the selection of less risky routes and adjusting insurance policies based on driving modes and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time risk assessment system is implemented, then safety and risk mitigation are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The risk assessment system divides the road network into discrete road segments, each with its own risk value. This segmentation allows the complex problem of overall route safety to be broken down into manageable individual segment assessments, reducing system complexity while maintaining comprehensive safety coverage.
Solution Approach 2:
The system pre-calculates risk values for road segments using historical accident data, geographic information, and vehicle information before vehicles actually travel these routes. This preliminary risk assessment enables real-time route selection without requiring complex real-time processing during vehicle operation.
2Measurement precision
If separate risk values are calculated for autonomous and manual driving modes, then measurement precision is improved, but loss of time and computational overhead increase
Solution Approach 1:
The system separates risk assessment into distinct categories for autonomous driving mode and manual driving mode, calculating specific risk values for each mode on each road segment. This segmentation enables precise mode-specific risk measurement while allowing pre-computation of both sets of values to minimize real-time processing delays.
Solution Approach 2:
The system dynamically adjusts risk values based on the driving mode parameter. By changing the risk assessment parameters according to whether the vehicle is in autonomous or manual mode, the system achieves precise mode-specific risk measurement without requiring completely separate assessment systems.
3Reliability
If real-time risk values are used for route selection, then safety is improved, but productivity and travel time may be reduced due to route optimization constraints
Solution Approach 1:
The system dynamically selects routes based on real-time risk values and driving mode, allowing the route choice to adapt to current conditions. This dynamic approach balances safety requirements with productivity considerations by selecting the most appropriate route rather than always choosing the safest or shortest option.
Solution Approach 2:
The route selection system uses risk values as a key parameter to determine the optimal route. By incorporating risk as a selectable parameter alongside distance and time, the system can optimize for different priorities (safety vs. speed) based on vehicle needs and conditions.
Data Source
AI summary
A route risk mitigation system and method using real-time information to improve the safety of vehicles operating in semi-autonomous or autonomous modes. The method mitigates the risks associated with driving by assigning real-time risk values to road segments and then using those real-time risk values to select less risky travel routes, including less risky travel routes for vehicles engaged in autonomous driving over the travel routes. The route risk mitigation system may receive location information, real-time operation information, (and/or other information) and provide updated associated risk values. In an embodiment, separate risk values may be determined for vehicles engaged in autonomous driving over the road segment and vehicles engaged in manual driving over the road segment.