Machine-Learned Route Safety Model for Collision Risk Prediction
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Solution Overview
Problem
Current navigation systems fail to accurately, effectively, and efficiently process dynamic features associated with different routes, such as varying levels of vehicular collision risk.
Innovation Solution
A machine-learned safety risk model is trained using accident and feature data of historical routes, enabling it to predict collision risk and guide route selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current route generation techniques are used, then routes can be provided from starting location to destination, but dynamic features such as vehicular collision risk cannot be accurately processed
Solution Approach 1:
A machine-learned safety risk model is introduced as an intermediary component between the route generation system and the dynamic feature data. This model processes accident data and route features to predict collision risks, enabling accurate risk assessment without requiring the entire navigation system to be fundamentally redesigned. The model acts as a specialized subsystem that handles the complex probability calculations and pattern recognition needed for accurate risk prediction.
Solution Approach 2:
The system transforms static route data into dynamic risk assessments by changing the parameter representation from simple geometric route definitions to probabilistic safety risk scores. The machine-learned model processes multiple parameters including accident history, route features, and environmental conditions to generate dynamic risk predictions that vary by route segment and time period, enabling accurate processing of dynamic features.
2Productivity
If traditional routing algorithms are used, then routes can be generated efficiently, but processing of dynamic risk features is ineffective
Solution Approach 1:
The machine-learned safety risk model is trained in advance using historical accident data and route features before being deployed for real-time route generation. This preliminary training phase allows the model to learn complex patterns and relationships in the data, so that during actual route generation, the model can quickly predict collision risks without slowing down the routing process. The heavy computational work is performed beforehand, maintaining efficiency during operational use.
Solution Approach 2:
The route is divided into multiple segments, and the safety risk model evaluates each segment independently to generate localized risk assessments. This segmentation allows the system to process dynamic features at a granular level, identifying high-risk portions of the route without having to analyze the entire route as a single unit. The segmented approach improves both efficiency and reliability by focusing computational resources on specific risk areas.
3Loss of time
If static route data is used, then routes can be provided quickly, but dynamic features like varying collision risk levels cannot be captured
Solution Approach 1:
The machine-learned safety risk model is designed to autonomously process route data and generate risk predictions without requiring manual updates or external intervention. The model self-adjusts by processing incoming accident data and route features, automatically updating its predictions for different route segments. This self-service capability allows the system to maintain adaptability to changing conditions while keeping route calculation times short, as the model operates independently once trained.
Data Source
AI summary
Systems and methods of configuring and using a machine-learned safety risk model to predict a corresponding risk of vehicular collision for different candidate routes are disclosed herein. In some example embodiments, a computer system obtains accident data and feature data for historical routes that have been communicated electronically as navigation guidance, trains a safety risk model using the accident data and the feature data of the historical routes as training data in a machine learning process, and then evaluates one or more routing algorithms by generating a corresponding set of routes for each routing algorithm and generating a corresponding performance measurement for each set of routes using the trained safety risk model.