公开(公告)号：US20250118204A1

公开(公告)日：2025-04-10

申请号：US18906612

申请日：2024-10-04

Applicant: Geotab Inc.

Inventor： Xinrong Zhou ,  Vinay Kiran Manjunath ,  Jiawei Yu ,  Meenakshi Sundaram Murugesan ,  Tuhin Tiwari ,  Willem Petersen ,  Xin Zhang ,  Gregory Gordon Douglas Hines

IPC: G08G1/16 ,  G08G1/01

Abstract: Disclosed herein are systems and methods for predicting collision risk associated with a roadway intersection. The methods may comprise operating at least one processor to: receive map data and telematics data originating from telematics devices installed in a plurality of vehicles; identify, using the map data, one or more roadway intersections; determine, using the telematics data and/or map data, for each of the one or more roadway intersections, one or more roadway intersection metrics thereof; determine a hazard rating for each roadway of each roadway intersection; and generate a collision probability for each roadway intersection by inputting into a machine learning model the one or more roadway intersection metrics and the hazard rating of each roadway thereof, the collision probability representing a risk of collision for a vehicle traversing the intersection.

公开(公告)号：US20250035457A1

公开(公告)日：2025-01-30

申请号：US18885909

申请日：2024-09-16

Applicant: Geotab Inc.

Inventor： Xin Zhang ,  Gregory Gordon Douglas Hines ,  Jiawei Yu ,  Willem Petersen ,  Tuhin Tiwari ,  Meenakshi Sundaram Murugesan ,  Javed Siddique ,  Jason Jiajie Yan ,  Narasimha Rao Durgam ,  Li Zhang ,  Vinay Kiran Manjunath ,  Xinrong Zhou ,  Yujie Chen ,  Chenyue Xu ,  Luis Perez Vazquez

IPC: G01C21/36 ,  G06N20/20 ,  H04W4/029

Abstract: Systems and methods for predicting collision probabilities are provided. The methods involve operating at least one processor to: retrieve vehicle data originating from a telematics device installed in a vehicle, the vehicle data including location data and a plurality of safety exception events performed by the vehicle, the plurality of safety exception events including a plurality of exception event types; identify a plurality of road network edges traveled by the vehicle based on the location data; determine an aggregated area collision rate based on the plurality of road network edges; determine a plurality of exception rates based on the vehicle data, each exception rate representing a normalized rate of occurrence of one of the exception event types; and determine a collision probability using at least one machine learning model on the plurality of exception rates and the aggregated area collision rate, the collision probability representing a risk of collision.