INRIX. has been granted a patent for a method that detects vehicle slowdowns by analyzing location data from vehicles. The process involves generating a space-time diagram and using a convolutional neural network to assess the likelihood of a slowdown, subsequently notifying affected vehicles. GlobalData’s report on INRIX gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on INRIX, AV navigation planning was a key innovation area identified from patents. INRIX's grant share as of June 2024 was 49%. Grant share is based on the ratio of number of grants to total number of patents.
Vehicle slowdown detection using location data and neural networks
The patent US12033500B2 outlines a method and system for detecting vehicle slowdowns on road segments using a computing device equipped with a processor. The method involves evaluating location data from vehicles to identify specific road segments and generating a space-time diagram that represents vehicle movement over time and distance. This diagram is processed through a neural network trained for image recognition, which detects slowdowns by constructing a time series of transition points based on features extracted from the diagram. The system can also assign colors to vehicle trajectories to indicate different speed ranges and apply filters to refine the data.
Additionally, the patent describes the transmission of notifications to drivers about identified slowdowns, including details such as the location of transition points and predicted time frames for the slowdown to dissipate. The system can factor in road and weather conditions, as well as historical data on slowdowns, to enhance accuracy. It also includes methods for clustering vehicle speeds to identify significant differences between free-flowing and slowed traffic, thereby pinpointing transition points along the road. Overall, the patent presents a comprehensive approach to real-time traffic monitoring and communication, aimed at improving driver awareness and traffic flow management.
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