AB Volvo has filed a patent for using neural networks to prevent jackknifing in articulated vehicles. The system receives operational parameters, identifies current surface conditions, predicts road conditions using a neural network, and modifies parameters based on distinctions between current and predicted conditions. The method involves receiving road condition data from tire sensors, determining current surface conditions, executing a machine learning algorithm to predict approaching surface conditions, and modifying operational parameters based on distinctions between current and approaching conditions. GlobalData’s report on AB Volvo gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on AB Volvo, Direct injection type engines was a key innovation area identified from patents. AB Volvo's grant share as of January 2024 was 48%. Grant share is based on the ratio of number of grants to total number of patents.
Adaptive braking system for articulated vehicles using neural networks
A recently filed patent (Publication Number: US20240034290A1) outlines a method for improving vehicle safety by utilizing tire sensors to gather road condition data and machine learning algorithms to predict approaching surface conditions. The method involves receiving data from tire sensors associated with both the vehicle and a trailer it is towing, determining the current surface condition, and modifying operational parameters based on the comparison between the current and approaching surface conditions. By incorporating inputs from other vehicles and executing a machine learning algorithm, the system can adjust parameters related to braking and slip ratios to enhance vehicle stability and control. Additionally, the method includes generating different operating modes based on the road conditions, with options like warning mode and poor surface mode, indicating the severity of required parameter modifications.
Furthermore, the patent extends to an articulated vehicle comprising a trailer, a truck, tire sensors, a processor, and a storage medium with instructions for implementing the method described. The articulated vehicle system allows for real-time adjustments to operational parameters based on road conditions and approaching surface conditions, ensuring optimal performance and safety. The machine learning algorithm utilized in the system is generated through a sensitivity analysis that considers various factors like GPS data, traffic data, route data, and weather data. By receiving road condition data from both tire sensors and other vehicles, as well as roadside infrastructure components, the system can effectively predict and adapt to changing road conditions, ultimately improving the overall safety and performance of the articulated vehicle.
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