Abstract: Systems, methods, and devices are disclosed for synchronizing a map within shared memory for a navigating autonomous vehicle. A map, defined by a geographical area around an autonomous vehicle navigating a route, can be received and stored in shared memory. The shared memory can be a specific physical block of RAM. Concurrent access to the map within the shared memory can be granted to downstream nodes on the autonomous vehicle as the autonomous vehicle navigates the route. This shares the map among many nodes while, at the same time, allowing updates to the map without blocking those nodes at any time.

Abstract: Systems, methods, and devices are disclosed for synchronizing a map within shared memory for a navigating autonomous vehicle. A map, defined by a geographical area around an autonomous vehicle navigating a route, can be received and stored in shared memory. The shared memory can be a specific physical block of RAM. Concurrent access to the map within the shared memory can be granted to downstream nodes on the autonomous vehicle as the autonomous vehicle navigates the route. This shares the map among many nodes while, at the same time, allowing updates to the map without blocking those nodes at any time.

Abstract: Systems, methods, and devices are disclosed for synchronizing a map within shared memory for a navigating autonomous vehicle. A map, defined by a geographical area around an autonomous vehicle navigating a route, can be received and stored in shared memory. The shared memory can be a specific physical block of RAM. Concurrent access to the map within the shared memory can be granted to downstream nodes on the autonomous vehicle as the autonomous vehicle navigates the route. This shares the map among many nodes while, at the same time, allowing updates to the map without blocking those nodes at any time.

Abstract: Systems and methods are provided for analyzing traffic signal lights in order to control an autonomous vehicle. A method includes receiving an image from a camera regarding at least one traffic signal light and receiving data related to the traffic signal light. Neural networks determine location and characteristics of the traffic signal light.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: obtaining ride preference information associated with a user, identifying a current vehicle pose, determining a motion plan for the vehicle along a route based at least in part on the ride preference information, the current vehicle pose, and vehicle kinematic and dynamic constraints associated with the route, and operating one or more actuators onboard the vehicle in accordance with the motion plan. The user-specific ride preference information influences a rate of vehicle movement resulting from the motion plan.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: selecting, by a controller onboard the vehicle, first data for a region from a first device onboard the vehicle based on a relationship between a time associated with the first data and a frequency associated with a second device, obtaining, by the controller, second data from the second device, the second data corresponding to the region, correlating, by the controller, the first data and the second data, and determining, by the controller, a command for operating one or more actuators onboard the vehicle in a manner that is influenced by the correlation between the first data and the second data.

Abstract: Methods and systems are provided for tracking an object. The system includes a data receiving module receiving two dimensional imaging data including an object and height map data correlating ground height and location. A two dimensions to three dimensions transformation module determines a location of the object in three dimensional space based on the two dimensional imaging data and the height map data. A tracking module tracks the object using the location of the object.

Abstract: Systems and methods for automatic maintenance of an autonomous vehicle include: identifying a maintenance requirement for an autonomous vehicle, wherein identifying a maintenance requirement includes using one or more sensors to obtain vehicle maintenance data; in response to identifying the maintenance requirement, generating a maintenance request comprising a description or data relating to the maintenance requirement; transmitting the maintenance request; receiving maintenance instructions comprising maintenance routing instructions and a maintenance schedule for a maintenance session for resolving the maintenance requirement; and implementing the maintenance instructions.

Abstract: Systems and methods are provided for analyzing vehicle signal lights in order to operate an autonomous vehicle. A method includes receiving an image from a camera regarding a vehicle proximate to the autonomous vehicle. Data from a lidar sensor regarding the proximate vehicle is used to determine object information for identifying a subsection within the camera image. The identified subsection corresponds to an area of the proximate vehicle containing one or more vehicle signals. One or more vehicle signal lights of the proximate vehicle is located by using the identified camera image subsection as an area of focus.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: obtaining ride preference information associated with a user, identifying a current vehicle pose, determining a motion plan for the vehicle along a route based at least in part on the ride preference information, the current vehicle pose, and vehicle kinematic and dynamic constraints associated with the route, and operating one or more actuators onboard the vehicle in accordance with the motion plan. The user-specific ride preference information influences a rate of vehicle movement resulting from the motion plan.

Abstract: Systems and methods are provided for analyzing traffic signal lights in order to control an autonomous vehicle. A method includes receiving an image from a camera regarding at least one traffic signal light and receiving data related to the traffic signal light. Neural networks determine location and characteristics of the traffic signal light.

Abstract: Methods and systems are provided for tracking an object. The system includes a data receiving module receiving two dimensional imaging data including an object and height map data correlating ground height and location. A two dimensions to three dimensions transformation module determines a location of the object in three dimensional space based on the two dimensional imaging data and the height map data. A tracking module tracks the object using the location of the object.

Abstract: A system and method for autonomous vehicle driving behavior modification include: receiving passenger driving behavior preferences for setting a driving behavior of an autonomous vehicle; generating driving behaviors controls based on the passenger driving behavior preferences; setting an initial driving behavior of the autonomous vehicle using the driving behavior controls, wherein setting the initial driving behavior comprises providing the driving behavior controls to be implemented by the autonomous vehicle during one or more routes involving the passenger; aggregating vehicle behavior feedback relating to a driving behavior of the autonomous vehicle during the one or more routes; and modifying the driving behavior of the autonomous vehicle based on the vehicle behavior feedback.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a visual position estimation method includes providing a ground model of an environment, and receiving, at a vehicle, sensor data relating to the environment, the sensor data including optical image data acquired by an optical camera coupled to the vehicle. A virtual camera image is generated based on the ground model, the position of the vehicle, and a position of the optical camera. The position of an object in the environment is determined based on the virtual camera image and the optical image data.

Abstract: Systems and methods are provided for analyzing vehicle signal lights in order to operate an autonomous vehicle. A method includes receiving an image from a camera regarding a vehicle proximate to the autonomous vehicle. Data from a lidar sensor regarding the proximate vehicle is used to determine object information for identifying a subsection within the camera image. The identified subsection corresponds to an area of the proximate vehicle containing one or more vehicle signals. One or more vehicle signal lights of the proximate vehicle is located by using the identified camera image subsection as an area of focus.

Abstract: Systems and methods for automatic maintenance of an autonomous vehicle include: identifying a maintenance requirement for an autonomous vehicle, wherein identifying a maintenance requirement includes using one or more sensors to obtain vehicle maintenance data; in response to identifying the maintenance requirement, generating a maintenance request comprising a description or data relating to the maintenance requirement; transmitting the maintenance request; receiving maintenance instructions comprising maintenance routing instructions and a maintenance schedule for a maintenance session for resolving the maintenance requirement; and implementing the maintenance instructions

Abstract: Systems and methods for autonomous vehicle parking includes: identifying parking parameters for parking an autonomous vehicle; receiving parking space data of one or more parking spaces; identifying a parking space of the one or more parking spaces for parking the autonomous vehicle; determining autonomous vehicle controls for controlling the autonomous vehicle to park at a parking space associated with the parking space data; implementing the autonomous vehicle controls for parking the autonomous vehicle in the parking space.

Abstract: A system and method for autonomous vehicle driving behavior modification include: receiving passenger driving behavior preferences for setting a driving behavior of an autonomous vehicle; generating driving behaviors controls based on the passenger driving behavior preferences; setting an initial driving behavior of the autonomous vehicle using the driving behavior controls, wherein setting the initial driving behavior comprises providing the driving behavior controls to be implemented by the autonomous vehicle during one or more routes involving the passenger; aggregating vehicle behavior feedback relating to a driving behavior of the autonomous vehicle during the one or more routes; and modifying the driving behavior of the autonomous vehicle based on the vehicle behavior feedback.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: selecting, by a controller onboard the vehicle, first data for a region from a first device onboard the vehicle based on a relationship between a time associated with the first data and a frequency associated with a second device, obtaining, by the controller, second data from the second device, the second data corresponding to the region, correlating, by the controller, the first data and the second data, and determining, by the controller, a command for operating one or more actuators onboard the vehicle in a manner that is influenced by the correlation between the first data and the second data.