Abstract: According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Abstract: According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Abstract: Example user identification systems and methods are described. In one implementation, a method determines a first step pulse associated with a user carrying a key fob and detects a user device proximate the key fob. The method further identifies a second step pulse measured by the user device and determines whether the first and second step pulses match. If the step pulses match, the user carrying the key fob is identified as the owner of the user device.

Abstract: According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Abstract: Systems and methods disclosed herein involve monitoring an interior state of a vehicle. A pre-ride image of an interior is recorded prior to initiating a ride A post-ride image of the interior is recorded following completion of the ride. A region of the post-ride image differing from the pre-ride image is detected, with the region of the post-ride image representing a change in the interior. A type of an object depicted in the region of the post-ride image is classified based on features extracted from the region of the post-ride image. In response to classifying the object depicted in the region of the post-ride image as a personal item, a prompt is served to the user to retrieve the object.

Abstract: Example user identification systems and methods are described. In one implementation, a method determines a first step pulse associated with a user carrying a key fob and detects a user device proximate the key fob. The method further identifies a second step pulse measured by the user device and determines whether the first and second step pulses match. If the step pulses match, the user carrying the key fob is identified as the owner of the user device.

Abstract: One variation of a method for identifying a user entering an autonomous vehicle includes: receiving a ride request from the user, the ride request specifying a pickup location; at the autonomous vehicle, autonomously navigating to the pickup location, scanning a field near the autonomous vehicle for a human approaching the autonomous vehicle, and, in response to detecting the human proximal the autonomous vehicle, recording an image of the human; detecting a face of the human in the image; accessing a faceprint characterizing facial features of the user; and, in response to the face of the human detected in the image exhibiting features represented in the faceprint, identifying the human as the user and triggering a door of the autonomous vehicle to unlock for the user.

Abstract: According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Abstract: One variation of a method for identifying a user entering an autonomous vehicle includes: receiving a ride request from the user, the ride request specifying a pickup location; at the autonomous vehicle, autonomously navigating to the pickup location, scanning a field near the autonomous vehicle for a human approaching the autonomous vehicle, and, in response to detecting the human proximal the autonomous vehicle, recording an image of the human; detecting a face of the human in the image; accessing a faceprint characterizing facial features of the user; and, in response to the face of the human detected in the image exhibiting features represented in the faceprint, identifying the human as the user and triggering a door of the autonomous vehicle to unlock for the user.

Abstract: According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Abstract: One variation of a method for monitoring an interior state of an autonomous vehicle includes: at the autonomous vehicle, recording a pre-ride image of an interior of the autonomous vehicle prior to initiating a ride; following completion of the ride, recording a post-ride image of the interior of the autonomous vehicle; detecting a region of the post-ride image differing from the pre-ride image, the region of the post-ride image representing a change in the interior of the autonomous vehicle following occupancy of the autonomous vehicle by a user; classifying a type of an object depicted in the region of the post-ride image based on features extracted from the region of the post-ride image; and in response to classifying the object depicted in the region of the post-ride image as a personal item, serving a prompt to the user to retrieve the object from the autonomous vehicle.

Abstract: According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

Abstract: The present invention extends to methods, systems, and computer program products for identifying a person as a driver of a vehicle. Aspects include using in-vehicle sensors to increase the accuracy of driver identification initially determined using other mechanisms. As such, at least two different types of sensory devices can be utilized to gather data in an effort to identify the driver. The data gathered from the first sensor (e.g., at a key fob) is processed to identify the driver based on learned characteristic patterns, such as the driver's gait, before the driver enters the vehicle. The data gathered from the second sensor (e.g., in an in-vehicle face or voice recognition system) is processed to confirm the driver. The confirmation is based on biometric data and learned characteristic patterns. Data from the second sensor is provided back to the first sensor to confirm the driver identity.