Collaborative Sensor Calibration for Autonomous Trucks
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Solution Overview
Problem
Current autonomous driving technologies do not adequately address the unique challenges of trucks and truck fleets, such as the need for advanced sensor calibration and precise monitoring of trailer positions, which are critical for safe navigation and obstacle avoidance, especially when sensors become misaligned during vehicle motion.
Innovation Solution
A method and system for collaborative sensor calibration on-the-fly using a fleet management center that coordinates between vehicles to assist in recalibrating sensors and estimating trailer positions, utilizing landmarks or other vehicles to maintain accurate sensor data and ensure safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional autonomous driving technologies are used, then general vehicle navigation is enabled, but sensor calibration accuracy and trailer position monitoring are insufficient for trucks
Solution Approach 1:
The patent implements truck-specific calibration procedures and trailer monitoring systems that are tailored to the unique geometric and operational characteristics of trucks and trailers, rather than using generic vehicle calibration methods. This includes specialized fiducial markers positioned on trailers and calibration routines that account for trailer articulation.
Solution Approach 2:
The system dynamically adjusts calibration parameters and monitoring thresholds based on truck operating conditions, trailer configurations, and environmental factors. Calibration parameters are modified to account for the larger dimensions, different sensor mounting positions, and varying operational states of trucks compared to standard vehicles.
2Productivity
If sensors are calibrated while the vehicle is stationary, then calibration accuracy is maintained, but the vehicle cannot operate efficiently in motion without frequent stops
Solution Approach 1:
The patent enables dynamic calibration of sensors while the truck is in motion by using moving fiducial markers on trailers and other vehicles as reference points. The calibration system continuously updates sensor parameters based on real-time relative position data between the ego vehicle and surrounding objects, eliminating the need for stationary calibration stops.
Solution Approach 2:
The calibration process operates continuously during vehicle operation rather than requiring periodic interruptions. The system maintains ongoing calibration by constantly analyzing spatial relationships between the truck, trailers, and surrounding environment, ensuring sensor accuracy is preserved throughout the entire operational cycle without reducing productivity.
3Reliability
If collaborative calibration using other vehicles or landmarks is implemented, then continuous calibration in motion is enabled, but system complexity increases
Solution Approach 1:
The patent uses fiducial markers as intermediary reference objects that simplify the calibration process. These markers are placed on trailers and other vehicles, providing easily detectable geometric features that serve as intermediaries between the sensor system and the physical environment. The markers translate complex real-world objects into standardized calibration targets that can be processed algorithmically.
Solution Approach 2:
The system creates simplified digital representations (copies) of physical landmarks and vehicles with known geometric properties. These digital models serve as reference frames for calibration, allowing the system to work with idealized geometric primitives rather than complex real-world objects. The copying approach reduces computational complexity while maintaining calibration accuracy.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for sensor calibration. A request is received from an ego vehicle in motion on a route for assistance in collaborative calibration of a sensor deployed on the ego vehicle. The request specifies a position of the ego vehicle and a configuration of the sensor with respect to the ego vehicle. A collaborative means along the route is identified based on the ego vehicle's position, the configuration of the sensor, the route, and the position associated with the collaborative means. A calibration assistance package is generated in response to the request and sent to the ego vehicle. The calibration assistance package includes information about the collaborative means that can be used to identify the collaborative means while in motion along the route for calibrating the sensor while the ego vehicle is in the vicinity of the collaborative means.