Lane-Level Occupancy Prediction for Real-Time Driving Recommendations
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Solution Overview
Problem
Conventional systems face challenges in tracking and predicting the future behavior of multiple road agents in real-time, making it difficult to avoid traffic incidents such as collisions.
Innovation Solution
A driving recommendation system that divides a roadway into granular lane-level cells, using a graph network to track occupancy and predict future cell status, rather than tracking road agents directly, allowing for the generation of driving recommendations based on predicted occupancy and risk estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems track and predict the future behavior of multiple road agents directly, then the system can identify potential traffic incidents, but the computational complexity and difficulty of real-time processing increase significantly
Solution Approach 1:
The patent segments the continuous roadway into discrete lane-level cells, transforming the problem from tracking multiple moving road agents to monitoring occupancy of static grid cells. This segmentation simplifies the computational model by converting complex trajectory predictions into simpler occupancy probability calculations for each cell, thereby reducing system complexity while maintaining incident detection capability
Solution Approach 2:
The patent introduces graph networks as an intermediary computational framework that bridges raw sensor data and driving recommendations. The graph network serves as a mediator that processes occupancy information across lane-level cells, enabling complex multi-agent behavior prediction through structured graph representations without requiring direct tracking of each road agent, thus reducing overall system complexity
2Measurement precision
If the system divides the roadway into granular lane-level cells and uses graph networks to track occupancy, then the prediction accuracy of future road agent behavior improves, but the data processing complexity increases
Solution Approach 1:
By dividing the roadway into discrete lane-level cells, the system achieves precise localization and tracking of road agents within each cell. This segmentation enables accurate prediction of future occupancy states while simplifying the computational model compared to continuous space tracking, as each cell can be processed independently through graph network operations
Solution Approach 2:
The patent transforms the prediction problem from tracking continuous road agent positions and velocities to predicting discrete occupancy states of lane-level cells. This parameter transformation simplifies the data structure and enables efficient graph network processing, reducing data processing complexity while maintaining or improving prediction accuracy through the structured cell-based representation
3Measurement precision
If the system processes graph network data to predict future cell occupancy status, then the ability to generate accurate driving recommendations improves, but the computational time and processing speed increase
Solution Approach 1:
The system performs preliminary processing by pre-defining the graph network structure representing lane-level cells before actual prediction occurs. This preliminary setup includes establishing cell boundaries, creating graph connections, and preparing the computational framework in advance, which enables faster real-time prediction of occupancy status and generation of driving recommendations without sacrificing accuracy
Solution Approach 2:
By segmenting the prediction problem into independent lane-level cell computations within the graph network, the system can process each cell's occupancy probability separately. This segmentation enables parallel computation strategies and optimizes processing efficiency, reducing overall computational time while maintaining accurate prediction of future road agent behavior through the structured cell-based approach
Data Source
AI summary
Systems and methods for generating driving recommendations are disclosed herein. One embodiment divides automatically a roadway into a plurality of lane-level cells; generates a graph network that represents the plurality of lane-level cells; gathers information pertaining to one or more detected road agents; projects onto the graph network the gathered information pertaining to the one or more detected road agents to update a current status of the plurality of lane-level cells; processes the graph network based on the updated current status of the plurality of lane-level cells to predict a future status of the plurality of lane-level cells, the predicted future status including at least occupancy, by a detected road agent, of the respective lane-level cells in the plurality of lane-level cells; and generates a driving recommendation based, at least in part, on the predicted future status of the plurality of lane-level cells.