Annotated Map Routing With Segment Risk Scoring for Autonomous Vehicles
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Solution Overview
Problem
Current autonomous vehicle navigation systems lack efficient and dynamic risk analysis methods for determining safe and optimal routes, leading to potential safety issues and suboptimal travel experiences.
Innovation Solution
The implementation of risk regression and trip classification techniques using machine learning and neural networks to compute fractional risk quantities for path segments, integrating dynamic and static conditions, and annotating navigation maps with risk factor cost data to select the lowest risk routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation systems are used without risk analysis, then the system complexity is low, but the safety and reliability of autonomous vehicle operation deteriorates
Solution Approach 1:
The patent segments the navigation system into multiple independent modules: path segment determination module, risk regression module, trip classification module, and route selection module. Each module performs a specific function (determining path segments, calculating risk scores, classifying trip types, selecting routes), allowing the complex safety analysis to be divided into manageable, modular components that can be developed and maintained independently while collectively providing comprehensive safety assessment
Solution Approach 2:
The patent introduces an intermediary annotation layer (cost layers) that sits between the basic map data and the route selection process. This annotation layer contains pre-calculated risk scores, trip classifications, and safety metrics for each path segment, serving as a mediator that translates complex safety analyses into simplified cost values that the route planning system can efficiently process and compare
2Manufacturing precision
If detailed ground truth data recording and processing is performed, then the manufacturing precision of maps improves, but the time and resources required deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-determining path segments and pre-calculating risk annotations for all possible routes before actual navigation occurs. The system pre-processes map data to identify all valid path segments, pre-calculates risk scores based on historical data and environmental factors, and pre-classifies trip types for different path segments. This preliminary preparation allows the autonomous vehicle to quickly select optimal routes during actual operation without performing time-consuming real-time analysis
Solution Approach 2:
The patent applies partial action by focusing computational resources on only the most relevant path segments and risk factors for each specific navigation task. Rather than analyzing all possible routes with equal detail, the system identifies and annotates only the path segments that are actually traversable or relevant to the current destination, performing detailed risk analysis only where needed while using simpler assessments for less critical segments
Data Source
AI summary
A control system for an autonomous vehicle can determine a risk value for each respective path segment of a plurality of path segments in a given area that includes a destination of the autonomous vehicle. The risk value can correspond to a cost layer in a map that includes the respective path segment. Based on the risk value for each respective path segment, the control system can determine a travel route for the autonomous vehicle to the destination, and autonomously control the autonomous vehicle to navigate along the travel route to the destination.