Autonomous Tractor Trailer Wind Disturbance Compensation
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Solution Overview
Problem
Autonomous vehicles lack systematic and reliable methods to estimate wind speed and direction in real-time, which affects their longitudinal and lateral performance, especially for semi-trucks, leading to potential loss of control and reduced fuel efficiency in windy conditions.
Innovation Solution
A dynamic wind compensation method that uses a processor to estimate wind speed and direction through data fusion of vehicle dynamics, image, sound, and third-party data, allowing for adaptive adjustments to control systems, such as lateral and longitudinal controls, to maintain desired motion and improve fuel efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wind compensation is implemented using multiple sensors and data fusion, then wind estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (anemometers, cameras, microphones, IMUs) and data sources (third-party weather data) into a unified wind estimation system. The processor fuses data from all these sources using algorithms to produce a comprehensive wind speed and direction estimate, resolving the contradiction by merging multiple measurement approaches rather than relying on a single sensor.
Solution Approach 2:
The system uses multi-functional sensors that serve multiple purposes. For example, cameras detect both road conditions and wind-induced vehicle motion, microphones detect both ambient noise and wind sound, and IMUs measure both vehicle dynamics and wind effects. This multi-functionality improves wind estimation accuracy without proportionally increasing system complexity.
2Reliability
If real-time wind data processing is performed using multiple sensors, then wind detection reliability is improved, but use of energy increases
Solution Approach 1:
The system performs wind estimation at specific intervals rather than continuously processing all sensor data at maximum rate. The processor receives and processes sensor data periodically to update wind speed and direction estimates, maintaining reliable wind detection while reducing overall energy consumption compared to continuous maximum-rate processing.
Solution Approach 2:
The system uses existing vehicle sensors that are already operational for other purposes (cameras for navigation, microphones for ambient awareness, IMUs for vehicle dynamics) to also provide wind estimation data. This self-service approach leverages already-powered components, minimizing additional energy consumption while improving wind detection reliability.
3Stability of the object's composition
If dynamic control modifications are made based on wind estimation, then vehicle stability is improved, but device complexity increases
Solution Approach 1:
The system implements a feedback loop where wind speed and direction estimates are continuously fed back to the vehicle controller, which then dynamically modifies lateral and longitudinal controls. This feedback mechanism maintains vehicle stability by continuously adjusting controls based on current wind conditions, resolving the contradiction through automated feedback rather than complex manual control systems.
Solution Approach 2:
The control system dynamically adjusts vehicle operation parameters (steering angle, acceleration, braking) in real-time based on estimated wind conditions. Rather than using a fixed complex control algorithm, the system adapts standard vehicle controls dynamically to compensate for wind effects, improving stability without requiring fundamentally new control mechanisms.
Data Source
AI summary
A method includes receiving, iteratively over time, sets of data including vehicle dynamics data, image data, sound data, third-party data, and wind speed sensor data, each detected at an autonomous vehicle and associated with a time period. The method also includes estimating a first wind speed and a first wind direction for each time period, in response to receiving the sets of data and based on the sets of data, via a processor of the autonomous vehicle. The method also includes iteratively modifying a lateral control and/or a longitudinal control of the autonomous vehicle based on the estimated first wind speed and the estimated first wind direction, via the processor of the autonomous vehicle and during operation of the autonomous vehicle.