Virtual Sensor Triggering for LiDAR-Camera Time Alignment
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Solution Overview
Problem
Conventional sensor coordination systems in autonomous vehicles face challenges in synchronizing sensor data due to communication latency, making it difficult to determine the precise alignment of sensors like LiDAR and cameras, which is crucial for accurate autonomous vehicle operations.
Innovation Solution
A virtual sensor system is developed that simulates the operation of a first sensor to predict alignment times with a second sensor, allowing for synchronized data capture by triggering the second sensor based on the predicted alignment, and iteratively refines this prediction using feedback to minimize errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor coordination systems are used to synchronize sensor data, then sensor data can be captured, but communication latency causes imprecise alignment between sensors
Solution Approach 1:
The system performs preliminary action by simulating the operation of the first sensor and predicting its alignment time with the second sensor before the actual alignment occurs. This allows the second sensor to be triggered at the precise moment of alignment, eliminating the delay caused by conventional communication-based coordination systems.
Solution Approach 2:
The system creates a virtual sensor system that copies and simulates the operation of the first sensor. This virtual model allows the system to predict alignment times without relying on real-time communication between physical sensors, thereby eliminating communication latency while maintaining measurement precision.
2Reliability
If sensor alignment is determined using conventional communication-based systems, then sensor coordination is achieved, but communication latency reduces synchronization accuracy
Solution Approach 1:
The system performs preliminary simulation and prediction of sensor alignment before the actual alignment event. By calculating the predicted alignment time in advance based on simulated sensor operation, the system ensures high synchronization accuracy without waiting for real-time communication feedback between sensors.
Solution Approach 2:
The virtual sensor system acts as an intermediary that mediates between the first and second sensors. Instead of relying on direct communication between physical sensors, the virtual model predicts alignment times and triggers the second sensor, eliminating communication latency while maintaining reliability.
3Measurement precision
If real-time sensor coordination is implemented, then sensor data synchronization is attempted, but communication latency prevents precise alignment determination
Solution Approach 1:
The system creates a simplified virtual copy of the first sensor's operation. This virtual model captures the essential behavior needed for alignment prediction without requiring complex real-time communication and coordination infrastructure, thereby maintaining measurement precision while reducing system complexity.
Solution Approach 2:
The system replaces the mechanical communication-based coordination system with a computational simulation approach. Instead of relying on real-time signal exchange between physical sensors, the virtual sensor model predicts alignment times through calculation, simplifying the overall system architecture while maintaining precision.
Data Source
AI summary
Described herein are systems, methods, and non-transitory computer readable media for triggering a sensor operation of a second sensor (e.g., a camera) based on a predicted time of alignment with a first sensor (e.g., a LiDAR), where operation of the second sensor is simulated to determine the predicted time of alignment. In this manner, the sensor data captured by the two sensors is ensured to be substantially synchronized with respect to the physical environment being sensed. This sensor data synchronization based on predicted alignment of the sensors solves the technical problem of lack of sensor coordination and sensor data synchronization that would otherwise result from the latency associated with communication between sensors and a centralized controller and/or between sensors themselves.