Abstract: Described herein are systems, methods, and other techniques for detecting an entrance point and an exit point for a site of interest using temporally-spaced satellite pings received from a set of transmitting vehicles. A geofence is obtained for the site of interest. A subset of the satellite pings that are within a set of cells along a border of the geofence are identified. A list of entrance satellite pings and a list of exit satellite pings are compiled using the subset of the satellite pings. The satellite pings from the list of entrance satellite pings and the list of exit satellite pings are counted for each of the set of cells and the entrance and exit points are assigned to cells respectively based on maximum values from the counts of entrance satellite pings and the counts of exit satellite pings.

Abstract: An image may be obtained from one or more cameras coupled to a first vehicle. The image may be provided as input to a machine learning algorithm configured to determine whether an object depicted in the image corresponds to another vehicle and to determine size information and location information for the object. Output from the machine learning algorithm enables obtaining features including size and location information for a second vehicle that is identified in the image. The features may be used to determine whether the second vehicle is depicted within a predetermined region of the image including a predicted travel path of the first vehicle. The features may also be used to determine whether the second vehicle is within a predetermined proximity of the first vehicle. Thereafter, a determination may be generated as to whether there is a significant risk of collision between the first vehicle and the second vehicle.

Abstract: An image may be obtained from one or more cameras coupled to a first vehicle. The image may be provided as input to a machine learning algorithm configured to determine whether an object depicted in the image corresponds to another vehicle and to determine size information and location information for the object. Output from the machine learning algorithm enables obtaining features including size and location information for a second vehicle that is identified in the image. The features may be used to determine whether the second vehicle is depicted within a predetermined region of the image including a predicted travel path of the first vehicle. The features may also be used to determine whether the second vehicle is within a predetermined proximity of the first vehicle. Thereafter, a determination may be generated as to whether there is a significant risk of collision between the first vehicle and the second vehicle.