Lead AV Obstacle Detection for Fleet Route Updates
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Solution Overview
Problem
Current autonomous vehicle navigation technologies fail to account for unexpected situations on the road, requiring driver intervention to navigate around obstacles or road conditions.
Innovation Solution
A lead autonomous vehicle equipped with sensors and a control subsystem identifies unexpected situations like unknown objects, road closures, or construction zones, updates its routing plan, and communicates with following vehicles to adjust their routes accordingly, using an operation server to confirm and update map data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current autonomous vehicle navigation technologies are used, then the system can operate with existing map data, but the system cannot handle unexpected situations on the road requiring driver intervention
Solution Approach 1:
The system performs preliminary actions by detecting unknown objects ahead of time using sensors, comparing them against map data, and proactively generating updated routing plans before the vehicle encounters the obstacle. This allows the autonomous vehicle to prepare navigation adjustments in advance, maintaining automation while handling unexpected situations.
Solution Approach 2:
The system implements feedback by continuously comparing sensor data with map data, detecting discrepancies that indicate unknown objects, and using this information to update routing plans. The operation server receives sensor data from multiple vehicles and provides updated map data back to the fleet, creating a closed-loop feedback system that improves adaptability while maintaining automation.
2Productivity
If the lead AV autonomously detects unknown objects and updates routing plans, then navigation efficiency improves, but system complexity increases due to sensor integration and real-time processing
Solution Approach 1:
The operation server acts as an intermediary that centralizes the complex processing tasks. Individual autonomous vehicles send sensor data to the operation server, which performs the computationally intensive tasks of detecting unknown objects, updating map data, and generating routing plans. This distributes the complexity from individual vehicles to a centralized system, improving navigation efficiency while managing system complexity through architectural separation.
Solution Approach 2:
The operation server provides multi-functionality by serving multiple autonomous vehicles simultaneously, performing object detection, map updates, and routing plan generation for the entire fleet. This universal system handles complex processing once and shares results across multiple vehicles, improving overall navigation efficiency while avoiding redundant complexity in each individual vehicle.
3Measurement precision
If the system compares sensor data with map data to detect unknown objects, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary comparison of sensor data with map data continuously in the background, so when an unknown object appears, the detection and processing can proceed immediately without delay. The operation server maintains an updated mental model of the environment by continuously processing map data, enabling rapid detection and response to unknown objects while maintaining high accuracy.
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 an unknown object within the field of view by comparing sensor data received from the sensor and a map data. The processor determines a lane occupied by the unknown object. The processor sends a first message to the following AVs comprising location coordinates of the unknown object and instructions to divert the lane. The processor instructs the lead AV to navigate around the unknown object. While navigating around the unknown object, the processor sends a plurality of second messages to an operation server, comprising sensor data related to the unknown object. The operation server updates the map data, indicating the unknown object at the location coordinates.