Lead AV Road Change Detection for Dynamic Fleet Rerouting
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Solution Overview
Problem
Current autonomous vehicle navigation technologies are not equipped to handle specific unexpected situations on roads, requiring manual driver intervention when encountering unknown objects, road closures, construction zones, or road structure changes.
Innovation Solution
A system comprising a lead autonomous vehicle and following vehicles, equipped with sensors and a control subsystem, that detects unexpected situations through sensor data comparison with map data, updates routing plans, and communicates with an operation server to inform and reroute following vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicle navigation systems use pre-loaded map data for routing, then navigation efficiency is improved, but the system cannot detect or respond to unexpected road conditions such as unknown objects, road closures, construction zones, or road structure changes
Solution Approach 1:
The system performs preliminary comparison of sensor data with map data to detect unexpected situations before they become critical navigation problems. By continuously comparing expected map features with actual sensor observations ahead of time, the system can identify unknown objects, road closures, construction zones, and road structure changes early, allowing proactive routing adjustments rather than reactive responses
Solution Approach 2:
The system establishes a feedback loop where sensor data from the vehicle's environment is continuously compared with pre-loaded map data, and the routing plan is dynamically updated based on detected discrepancies. This closed-loop feedback mechanism enables the navigation system to adapt to unexpected road conditions by detecting anomalies, determining their nature, and automatically adjusting the routing plan to avoid hazards while maintaining efficient navigation
2Reliability
If the system continuously monitors and compares sensor data with map data to detect unexpected situations, then detection reliability is improved, but computational complexity and processing time increase
Solution Approach 1:
The system extracts only the necessary comparison between sensor data and map data for detecting unexpected situations, rather than processing all possible navigation parameters. By focusing specifically on identifying discrepancies between expected and observed road features, the system achieves reliable detection of unknown objects, road closures, construction zones, and road structure changes while minimizing unnecessary computational overhead
Solution Approach 2:
The system applies detection algorithms selectively to specific regions and features where unexpected situations are most likely to occur, such as comparing sensor data with map data in the vehicle's forward path and areas with known construction activity. This localized approach maintains high detection reliability for critical hazards while reducing overall computational complexity by not uniformly processing all map data
3Reliability
If the lead autonomous vehicle detects and navigates around unexpected objects by diverting from the current lane, then navigation safety is improved, but the routing plan complexity increases
Solution Approach 1:
The routing plan is made dynamic rather than static, allowing the lead autonomous vehicle to automatically adjust its path in real-time when unexpected objects are detected. The system dynamically modifies the routing plan by determining the nature of detected anomalies, calculating safe alternative paths that divert from the current lane, and continuously updating navigation instructions based on the vehicle's changing environment, thereby maintaining safety without requiring overly complex pre-planned routes
Data Source
AI summary
A lead autonomous vehicle (AV) includes a sensor configured to observe a field of view in front of the lead AV. Following AVs are on the same road behind the lead AV. A processor of the lead AV is configured to detect a road structural change on the particular road. The processor updates driving instructions of the lead AV to navigate through the structural change using driving instructions related to the structural change. The processor sends a first message to the operation server indicating that the structural change is detected. The operation server updates the first portion of the map data, reflecting the structural change. The operation server sends the updated map data to the one or more following AVs.