Autonomous Vehicle Path Planning Around Road Rutting
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Solution Overview
Problem
Roads deteriorate due to factors like weather and vehicle wear, with trucks causing significant rutting that is costly and dangerous, necessitating a systematic method to prevent and avoid rutting in high-traffic areas.
Innovation Solution
An anti-rutting system for autonomous vehicles that collects data to identify road deterioration features, generates a map of these features, and adjusts its path to avoid them, using sensor data and machine learning models to minimize contact with ruts and other damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If trucks repeatedly pass over the same location on a road, then transportation efficiency and productivity are improved, but road deterioration and rutting worsen
Solution Approach 1:
The system dynamically adjusts the routing of autonomous trucks based on real-time road condition data. When rutting is detected in high-traffic areas, the system dynamically reroutes vehicles to alternative paths, transforming the static routing problem into a dynamic adaptation process that prevents further deterioration while maintaining transportation productivity
Solution Approach 2:
The system implements a feedback loop where sensor data from autonomous vehicles continuously monitors road conditions, identifies rutting patterns, and feeds this information back to the routing algorithm. This closed-loop feedback enables the system to learn from past road damage and adjust future routing decisions to prevent recurring deterioration
2Reliability
If autonomous vehicles collect and process sensor data to identify road deterioration features, then road safety and reliability are improved, but system complexity and computational requirements worsen
Solution Approach 1:
The system segments the complex task of road deterioration detection into distinct modules: sensor data acquisition, feature extraction, rutting pattern recognition, and routing decision-making. Each module handles a specific aspect of the problem, reducing overall system complexity while maintaining high reliability through specialized processing at each stage
Solution Approach 2:
The system introduces an intermediary processing layer that acts as a mediator between raw sensor data and routing decisions. This intermediary layer processes and interprets sensor data to identify road deterioration features, shielding the core routing system from the complexity of raw data processing while ensuring reliable safety information is transmitted
Data Source
AI summary
An anti-rutting system is provided. The anti-rutting system includes a processor and a memory. The processor is configured to receive, from one or more autonomous vehicles, sensor data indicating conditions of roads traveled by the one or more autonomous vehicles, generate, based on the sensor data, a model of a surface of the roads traveled by the one or more autonomous vehicles, identify one or more road deterioration features from the model, generate a map indicating locations of the road deterioration features, and transmit the map to the one or more autonomous vehicles, wherein the one or more autonomous vehicles are configured to generate constraints for a planned path of the autonomous vehicle to avoid contact with the locations of the road deterioration features identified in the map while operating.