Haptic Road-Feature Calibration for Autonomous Vehicle Sensors
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Solution Overview
Problem
Existing vehicle sensor systems face orientation errors due to assembly tolerances and operational factors like wear and damage, leading to performance deterioration and safety hazards in autonomous driving.
Innovation Solution
A system utilizing haptic sensors to detect road features and provide ground-truth locations for sensor calibration, enabling precise calibration and localization during vehicle operation without additional infrastructure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional vehicle assembly techniques are used with standard tolerances, then manufacturing cost and ease of assembly are improved, but sensor orientation accuracy deteriorates
Solution Approach 1:
The system performs preliminary calibration actions by establishing ground-truth locations of road features before using them to correct sensor orientation errors. The autonomy computing system pre-processes haptic sensor data to identify road feature locations, then uses this pre-established information to calibrate sensors, thereby improving orientation accuracy without requiring tighter manufacturing tolerances
Solution Approach 2:
The patent replaces mechanical alignment methods with a computational calibration system. Instead of relying on precise mechanical sensor mounting, the system uses haptic sensors to detect road features and computationally determines ground-truth locations, then uses these to calculate and correct orientation errors through algorithmic processing rather than mechanical adjustment
2Duration of action of stationary object
If sensor orientation is allowed to drift during operation due to wear and vibrations, then device durability and operational flexibility are improved, but measurement accuracy deteriorates
Solution Approach 1:
The system implements continuous feedback calibration by constantly monitoring haptic sensor responses to road features and comparing detected positions with ground-truth locations. This feedback loop enables the autonomy computing system to detect orientation drift caused by wear and vibrations, then automatically correct it, maintaining measurement precision throughout the vehicle's operational life
Solution Approach 2:
The calibration system performs self-service by using the vehicle's own haptic sensors and existing road infrastructure to automatically detect and correct its own sensor orientation errors. The system independently identifies ground-truth locations through haptic feedback and uses this information to recalibrate sensors without external intervention, enabling continuous self-correction during operation
3Measurement precision
If dedicated calibration infrastructure is deployed to improve sensor calibration accuracy, then measurement precision is improved, but system complexity and cost increase
Solution Approach 1:
The system achieves universal calibration capability by using existing haptic sensors designed for other purposes (collision avoidance, comfort control) to perform calibration functions. The same haptic sensors that detect road surface conditions for vehicle control are also used to identify road features for calibration, eliminating the need for dedicated calibration hardware and reducing system complexity
Solution Approach 2:
The patent uses road features as an intermediary medium for calibration. Instead of requiring direct interaction with complex calibration infrastructure, the system uses naturally occurring or existing road features (markings, edges, bumps) as intermediate reference points. These intermediaries provide ground-truth locations that bridge the gap between the vehicle's sensor frame and the external world, enabling accurate calibration without complex dedicated infrastructure
4Manufacturing precision
If offline calibration methods are used to ensure sensor accuracy, then manufacturing precision is improved, but productivity and response time to operational errors deteriorate
Solution Approach 1:
The system transitions from static offline calibration to dynamic online calibration. The calibration process is made adaptive and continuous, updating sensor orientation parameters in real-time based on current haptic sensor data and detected road features. This dynamic approach allows the system to respond to operational conditions and correct drift as it occurs, improving both productivity and maintaining precision
Solution Approach 2:
The calibration system operates continuously during vehicle operation rather than being limited to periodic offline calibration events. By continuously processing haptic sensor data and detecting road features, the system maintains uninterrupted calibration, ensuring sensor accuracy is constantly updated and corrected, thereby improving productivity while maintaining the precision benefits of thorough calibration
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances sensor calibration and localization accuracy by providing real-time, precise ground-truth data, reducing errors and improving safety in autonomous vehicles.
Implementation Method 1
detect, based on a haptic response received from the haptic sensor, a road feature over which the autonomous vehicle travels
Data Source
AI summary
A system for online sensor calibration of an autonomous vehicle is provided. The system includes a processor in communication with a memory device and a haptic sensor of the autonomous vehicle. The processor is programmed to detect, based on a haptic response received from the haptic sensor, a road feature over which the autonomous vehicle travels, and identify a location of the road feature as a ground-truth location of the autonomous vehicle at a time at which the autonomous vehicle traveled over the road feature. The processor is also programmed to calibrate at least one other sensor of the autonomous vehicle using the ground-truth location of the autonomous vehicle.