Artificially intelligent mobility safety system

Abstract

In some examples, a mobility safety system includes a real-time data capturing component to capture data relating to conditions within a traffic environment using at least one of a camera, a radar sensor, a LiDAR sensor, a proximity sensor, an inertial measurement unit (IMU), and a global positioning system (GPS), at least one trained model to generate a risk estimation related to a mobility platform within the traffic environment, the at least one trained model being trained using at least one of supervised, unsupervised and semi-supervised learning, a risk estimation component to generate the risk estimation, and an alert activation component to generate a first alert directed at an operator of the mobility platform based on the risk estimation.

What is claimed is: 
     
         1 . A method to operate a mobility safety system of a mobility platform, the method comprising: 
 capturing real-time data relating to conditions within a traffic environment;   using at least one trained model, generating a risk estimation related to a mobility platform within the traffic environment; and   based on the risk estimation, generating a first alert directed at at least an operator of the mobility platform.   
     
     
         2 . The method of  claim 1 , based on the risk estimation, generating a second alert directed at an operator of a further actor within the traffic environment. 
     
     
         3 . The method of  claim 2 , wherein the second alert directed at the operator of the further actor within the traffic environment is an audible alert having a loudness exceeding 100 decibels. 
     
     
         4 . The method of  claim 1 , comprising, based on the risk estimation, selectively recording the real-time data related to the conditions within the traffic environment. 
     
     
         5 . The method of  claim 1 , comprising, based on the risk estimation, selectively performing computations with the mobility safety system. 
     
     
         6 . The method of  claim 1 , wherein capturing the real-time data relating to the conditions within the traffic environment comprises capturing the real-time data using at least one of a camera to capture image data, a radar sensor to capture radar data, a LiDAR sensor to capture lidar data, a proximity sensor to capture proximity data, an inertial measurement unit (IMU) to capture IMU data, and a global positioning system (GPS) to capture GPS data. 
     
     
         7 . The method of  claim 1 , wherein the generating of the risk estimation comprises:
 performing a localization and mapping operation using the real-time data to generate localization and mapping data;   performing a perception operation using the real-time data and the localization and mapping data to generate object tracking and range estimation data;   performing a prediction operation real-time data using the localization and mapping data and the object tracking and range estimation data to generate prediction data related to the traffic environment; and   performing a risk estimation operation, to generate the risk estimation, using the real-time data, the localization and mapping data, the object tracking and range estimation data, and the prediction data.   
     
     
         8 . The method of  claim 7 , wherein the localization and mapping operation comprises: 
 accessing the real-time data relating to the conditions within the traffic environment;   performing an iterative position and orientation estimation for the mobility safety system using at least one of GPS data, IMU data, visual odometry data derived from image data, and a motion model; and   outputting ego-vehicle position and orientation data related to the mobility safety system.   
     
     
         9 . The method of  claim 8 , wherein the perception operation comprises:
 accessing the real-time data relating to the conditions within the traffic environment;   accessing the ego-vehicle position and orientation data;   performing an object detection operation to identify objects within the traffic environment;   performing an orientation estimation operation, responsive to identification of an object within the traffic environment, to estimate an orientation of the detected object;   performing a ground surface estimation operation, using the real-time data and environmental map data, to estimate geometry of a ground surface within the traffic environment;   performing a range estimation operation, using the estimated geometry of the ground surface and the real-time data, to estimate distances to identify the objects within the traffic environment; and   outputting object estimate data based on the estimated orientation, the estimated geometry, and the estimated distances.   
     
     
         10 . The method of  claim 9 , wherein the prediction operation comprises:
 accessing the real-time data relating to the conditions within the traffic environment;   accessing the ego-vehicle position and orientation data;   accessing the object estimate data;   performing an object motion prediction to generate object production data;   performing an interaction prediction to generate interaction production data; and   outputting the prediction data including the object motion prediction data and the interaction prediction data.   
     
     
         11 . The method of  claim 10 , wherein the risk estimation operation comprises:
 accessing the real-time data relating to conditions within the traffic environment;   accessing the ego-vehicle position and orientation data;   accessing the object estimate data;   accessing the prediction data;   performing a collision estimation operation to generate collision probability data reflecting a probability of a collision between the objects within the traffic environment;   performing a collision severity operation to generate collision severity data reflecting a probable severity of the collision between the objects within the traffic environment;   performing a predictive collision risk operation to generate predictive collision risk data; and   outputting the predictive collision risk data, the predictive collision risk data comprising the risk estimation.   
     
     
         12 . The method of  claim 7 , wherein each of the localization and mapping operation, the perception operation, the prediction operation, and the risk estimation operation are performed using a respective trained model. 
     
     
         13 . The method of  claim 7 , wherein two or more of the localization and mapping operation, the perception operation, the prediction operation, and the risk estimation operation are performed using a unified trained model. 
     
     
         14 . The method of  claim 1 , wherein the at least one trained model is trained using at least one of supervised, unsupervised and semi-supervised learning. 
     
     
         15 . A computing apparatus comprising:
 at least one processor; and   at least one memory storing instructions that, when executed by the at least one processor, configure the apparatus to: 
 capture real-time data relating to conditions within a traffic environment; 
 using at least one trained model, generating a risk estimation related to a mobility platform within the traffic environment; and 
 based on the risk estimation, generating a first alert directed at at least an operator of the mobility platform. 
   
     
     
         16 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by at least one computer, cause the at least one computer to:
 capture real-time data relating to conditions within a traffic environment;   using at least one trained model, generating a risk estimation related to a mobility platform within the traffic environment; and   based on the risk estimation, generating a first alert directed at at least an operator of the mobility platform.   
     
     
         17 . A mobility safety system comprising:
 a real-time data capturing component to capture data relating to conditions within a traffic environment using at least one of a camera, a radar sensor, a LiDAR sensor, a proximity sensor, an inertial measurement unit (IMU), and a global positioning system (GPS);   at least one trained model to generate a risk estimation related to a mobility platform within the traffic environment, the at least one trained model being trained using at least one of supervised, unsupervised and semi-supervised learning;   a risk estimation component to generate the risk estimation; and   an alert activation component to generate a first alert directed at an operator of the mobility platform based on the risk estimation.