Road Quality Routing Using Damage Prediction for Autonomous Vehicles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in determining which roads to take and how to navigate them efficiently while minimizing damage and passenger discomfort, as they lack the human judgment to assess and respond to varying road qualities.
Innovation Solution
The system uses sensor data from various systems like camera, lidar, and radar to assess road quality, and a remote computing system determines routes that avoid poor road segments, providing passengers with options between smoother and quicker routes, using machine learning models to predict damage and optimize navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use standard routing systems, then navigation efficiency is maintained, but vehicle damage and passenger discomfort increase due to poor road quality
Solution Approach 1:
The system performs preliminary assessment of road quality before the vehicle reaches those segments. Sensors detect road conditions ahead of time, and the routing system pre-calculates alternative paths to avoid poor quality roads, allowing the vehicle to navigate efficiently while minimizing damage.
Solution Approach 2:
A remote computing system acts as an intermediary between the vehicle's sensor data and routing decisions. The remote system receives sensor data, processes road quality information, and generates optimized routes that balance vehicle protection with navigation efficiency.
2Reliability
If autonomous vehicles avoid all poor quality roads, then vehicle damage is minimized, but travel time increases significantly
Solution Approach 1:
The system applies different routing strategies to different segments of the journey. Instead of avoiding all poor quality roads uniformly, it selectively avoids segments based on local road quality assessments, vehicle type, and passenger preferences, optimizing the balance between protection and time.
Solution Approach 2:
The system provides passengers with partial control over route selection by offering multiple route options (e.g., smoothest route, quickest route, balanced route). This allows passengers to choose their preferred level of vehicle protection versus travel time trade-off.
3Measurement precision
If the system collects and processes extensive sensor data for road quality assessment, then routing accuracy improves, but system complexity increases
Solution Approach 1:
The system merges data from multiple sensor types (cameras, lidars, radars, suspension sensors) into a unified road quality assessment model. By combining these diverse data sources, the system achieves comprehensive and accurate road quality measurement while managing complexity through integrated processing.
Solution Approach 2:
The system uses the autonomous vehicle's existing sensor suite, which is already deployed for other autonomous driving functions. This self-service approach allows the vehicle to assess road quality using sensors it already carries, avoiding the need for additional dedicated hardware and reducing overall system complexity.
Data Source
AI summary
Aspects of the disclosed technology provide solutions for performing vehicle routing based on road quality data. In some approaches, a process of the technology can include steps for collecting road quality data using at least one vehicle-mounted sensor, identifying two or more routes to a destination location, receiving historical road quality data associated with the two or more routes to the destination location, and calculating a damage projection associated with each of the two or more routes to the destination location, wherein the damage projection is based on the historical road quality data for at least one of the two or more routes to the destination location.