Abstract: A method for identifying hazardous conditions during vehicle operation includes: accessing data from a sensor corresponding to images of one or more other vehicles; computing, with a machine-learned model, a threat estimate for the one or more other vehicles based at least in part on the data from the sensor; and transmitting a threat alert to a driver interface of an ego vehicle.

Abstract: A parking detection method includes: accessing data corresponding to an image from a camera on a vehicle; computing a confidence score for a possible vacant parking location from the data corresponding to the image from the camera on the vehicle; and when the confidence score is greater than a threshold confidence, identifying the possible vacant parking location as an actual vacant parking location from the data corresponding to the image from the camera on the vehicle.

Abstract: A method of developing a road conditioning monitoring model is disclosed. The method comprises utilizing vehicle sensors to collect data under controlled conditions over a road surface to develop a training data set. The training data set undergoes a data analysis to determine an absolute running mean associated with the first road surface under controlled conditions. Abnormalities in the road surface are detected by comparing the absolute running mean with the training data set. The detected abnormalities are classified to create road surface classification rules. A method of developing a road condition database is also disclosed, that comprises utilizing vehicle sensors during operation of a vehicle to collect data during the operation and feeding the data into the road conditioning monitoring model to classify the road conditions on which the vehicle is driven. A system for determining road condition impact on a vehicle is also disclosed.

Abstract: A method for identifying a vehicle of interest includes: accessing data corresponding to a vehicle of interest alert; accessing data from a sensor corresponding to images of one or more other vehicles; computing, with a machine-learned model, a vehicle of interest match estimate for the one or more other vehicles based at least in part on the data from the sensor; and computing a vehicle of interest identification.

Abstract: A method for identifying hazardous conditions during vehicle operation includes: accessing data from a sensor corresponding to images of one or more other vehicles; computing, with a machine-learned model, a threat estimate for the one or more other vehicles based at least in part on the data from the sensor; and transmitting a threat alert to a driver interface of an ego vehicle.

Abstract: A method of mitigating aquaplaning by a vehicle is disclosed. The method comprises creating an aquaplaning identification model using simulated data and deploying the model in a vehicle onboard computer. During vehicle operation, data is collected from one or more image sensors installed on the vehicle and directed to the model disposed on the onboard computer. The results of the model are deployed to a server cloud wherein the results are processed in a neural net and the model is updated in the server cloud and an updated model is deployed to the vehicle onboard computer in a software loop. The method of developing the model is also disclosed.

Abstract: A parking detection method includes: accessing data corresponding to an image from a camera on a vehicle; computing a confidence score for a possible vacant parking location from the data corresponding to the image from the camera on the vehicle; and when the confidence score is greater than a threshold confidence, identifying the possible vacant parking location as an actual vacant parking location from the data corresponding to the image from the camera on the vehicle.

Abstract: A method of developing a road conditioning monitoring model is disclosed. The method comprises utilizing vehicle sensors to collect data under controlled conditions over a road surface to develop a training data set. The training data set undergoes a data analysis to determine an absolute running mean associated with the first road surface under controlled conditions. Abnormalities in the road surface are detected by comparing the absolute running mean with the training data set. The detected abnormalities are classified to create road surface classification rules. A method of developing a road condition database is also disclosed, that comprises utilizing vehicle sensors during operation of a vehicle to collect data during the operation and feeding the data into the road conditioning monitoring model to classify the road conditions on which the vehicle is driven. A system for determining road condition impact on a vehicle is also disclosed.