Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: The present disclosure provides systems and methods for operating autonomous vehicles. In one example embodiment, the method includes receiving an instruction for an autonomous vehicle to autonomously travel in an autonomous mode from a first location to a second location. When operating in the autonomous mode, the autonomous vehicle is restricted to operating within a limited operating envelope. The method includes receiving data indicative of a request by a human operator to operate the autonomous vehicle. In response to receiving the data indicative of the request by the human operator to operate the autonomous vehicle, the method includes authorizing the autonomous vehicle for operation by a human operator in a non-autonomous mode outside of the limited operating envelope. The limited operating envelope limits at least one operating capability of the autonomous vehicle based at least in part on an operating limit associated with an autonomous system for the autonomous vehicle.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: A method for processing destination change requests from passengers of autonomous vehicles can include determining one or more operating parameters for the autonomous vehicle, and based on the one or more operating parameters, determining a feasibility indicator for the autonomous vehicle to travel to the second destination. Based on the feasibility indicator, the method includes determining a suggested destination for the passenger and transmit an instruction to a computing system of the autonomous vehicle causing the autonomous vehicle to reroute to the suggested destination. The method includes transmitting a set of travel directions to a computing device of the passenger, indicating a travel route from the suggested destination to the second destination.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: A method can include obtaining data associated with a destination change request from a passenger of the autonomous vehicle. The destination change request can be indicative of a request to change a first destination to a second destination. In response to obtaining data associated with the destination change request, the method can include: obtaining data indicative of one or more operating parameters for the autonomous vehicle; determining a feedback response for responding to the destination change request based at least in part on the one or more operating parameters; and providing data associated with the feedback response for presentation via the one or more interfaces to the passenger of the autonomous vehicle. The feedback response can include a suggested destination. The suggested destination can be different from the second destination.

Abstract: An on-demand transport facilitation system can receive transport requests from requesting users throughout a given region, and select autonomous vehicles (AVs) and human driver to service the transport requests. The AV can operate on a mapped and labeled autonomy grid within the given region. For a given transport request, the transport system can determine an optimal pick-up location along the autonomy grid based on the current location of the requesting user and a current location of a selected AV, and transmit data indicating walking directions from the current location of the requesting user to the optimal pick-up location. The transport system may then coordinate the rendezvous by monitoring progress made by the requesting user and AV to the optimal pick-up location, and controlling the pace of the AV.

Abstract: An on-demand transport facilitation system can receive transport requests from requesting users throughout a given region, and select autonomous vehicles (AVs) and human driver to service the transport requests. The AV can operating on a mapped and labeled autonomy grid within the given region. For a given transport request, the transport system can determine an optimal pick-up location along the autonomy grid based on the current location of the requesting user and a current location of a selected AV, and transmit data indicating walking directions from the current location of the requesting user to the optimal pick-up location. The transport system may then coordinate the rendezvous by monitoring progress made by the requesting user and AV to the optimal pick-up location, and controlling the pace of the AV.

Abstract: An on-demand transport system can manage an on-demand transportation service for a given region by matching requesting users with drivers and the AVs, where the AVs utilize localization maps and live sensor data to autonomously operate throughout the given region. The transport system can identify a local anomaly within the given region that affects AV performance. The transport system can transmit a routing invitation a driver to provide feedback corresponding to the local anomaly. Based on feedback data received from the driver, the transport system can transmit an update to AVs intersecting the local anomaly to enable the intersecting AVs to resolve the local anomaly.

Abstract: An on-demand transport system can manage an on-demand transportation service for a given region by matching requesting users with drivers and the AVs, where the AVs utilize localization maps and live sensor data to autonomously operate throughout the given region. The transport system can identify a local anomaly within the given region that affects AV performance. The transport system can transmit a routing invitation a driver to provide feedback corresponding to the local anomaly. Based on feedback data received from the driver, the transport system can transmit an update to AVs intersecting the local anomaly to enable the intersecting AVs to resolve the local anomaly.

Abstract: A LIDAR device may transmit light pulses originating from one or more light sources and may receive reflected light pulses that are then detected by one or more detectors. The LIDAR device may include a lens that both (i) collimates the light from the one or more light sources to provide collimated light for transmission into an environment of the LIDAR device and (ii) focuses the reflected light onto the one or more detectors. The lens may define a curved focal surface in a transmit path of the light from the one or more light sources and a curved focal surface in a receive path of the one or more detectors. The one or more light sources may be arranged along the curved focal surface in the transmit path. The one or more detectors may be arranged along the curved focal surface in the receive path.