Abstract: A system includes an autonomous vehicle (AV) comprising a sensor, a control subsystem, and an operation server. The control subsystem receives sensor data comprising location coordinates of the AV from the sensor. The operation server detects an unexpected event from the sensor data, comprising at least one of an accident, an inspection, and a report request. The operation server receives a message from a user comprising a request to access particular information regarding the AV and location data. The operation server associates the AV with the user if the location coordinates of the AV match location data of the user. The operation server establishes a communication path between the user and a remote operator for further communications.

Abstract: A system and method for fisheye image processing can be configured to: receive fisheye image data from at least one fisheye lens camera associated with an autonomous vehicle, the fisheye image data representing at least one fisheye image frame; partition the fisheye image frame into a plurality of image portions representing portions of the fisheye image frame; warp each of the plurality of image portions to map an arc of a camera projected view into a line corresponding to a mapped target view, the mapped target view being generally orthogonal to a line between a camera center and a center of the arc of the camera projected view; combine the plurality of warped image portions to form a combined resulting fisheye image data set representing recovered or distortion-reduced fisheye image data corresponding to the fisheye image frame; generate auto-calibration data representing a correspondence between pixels in the at least one fisheye image frame and corresponding pixels in the combined resulting fisheye image

Abstract: The disclosed technology enables automated parking of an autonomous vehicle. An example method of performing automated parking for a vehicle comprises obtaining, from a plurality of global positioning system (GPS) devices located on or in an autonomous vehicle, a first set of location information that describes locations of multiple points on the autonomous vehicle, where the first set of location information are associated with a first position of the autonomous vehicle, determining, based on the first set of location information and a location of the parking area, a trajectory information that describes a trajectory for the autonomous vehicle to be driven from the first position of the autonomous vehicle to a parking area, and causing the autonomous vehicle to be driven along the trajectory to the parking area by causing operation of one or more devices located in the autonomous vehicle based on at least the trajectory information.

Abstract: A vehicle position and velocity estimation system based on camera and LIDAR data is disclosed. An embodiment includes: receiving input object data from a subsystem of a vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device, the distance measuring device comprising one or more LIDAR sensors; determining a first position of a proximate object near the vehicle from the image data; determining a second position of the proximate object from the distance data; correlating the first position and the second position by matching the first position of the proximate object detected in the image data with the second position of the same proximate object detected in the distance data; determining a three-dimensional (3D) position of the proximate object using the correlated first and second positions; and using the 3D position of the proximate object to navigate the vehicle.

Abstract: An autonomous vehicle can classify and prioritize agent of interest (AOI) objects located around the autonomous vehicle to manage computational resources. An example method performed by an autonomous vehicle includes determining, based on a location of the autonomous vehicle and based on a map, an area in which the autonomous vehicle is operated, determining, based on sensor data received from sensors located on or in the autonomous vehicle, attributes of objects located around the autonomous vehicle, where the attributes include information that describes a status of the objects located around the autonomous vehicle, selecting, based at least on the area, a classification policy that includes a plurality of rules that are associated with a plurality of classifications to classify the objects, and for each of the objects located around the autonomous vehicle: monitoring an object according to a classification of the object based on the classification policy.

Abstract: Disclosed are devices, systems and methods for using a rotating camera for vehicular operation. One example of a method for improving driving includes determining, by a processor in the vehicle, that a trigger has activated, orienting, based on the determining, a single rotating camera towards a direction of interest, and activating a recording functionality of the single rotating camera, where the vehicle comprises the single rotating camera and one or more fixed cameras, and where the single rotating camera provides a redundant functionality for, and consumes less power than, the one or more fixed cameras.

Abstract: A method includes receiving a series of road images from a side-view camera sensor of the autonomous driving vehicle. For each object from objects captured in the series of road images, a series of bounding boxes in the series of road images is generated, and a direction of travel or stationarity of the object is determined. The methods and apparatus also include determining a speed of each object for which the direction of travel has been determined and determining, based on the directions of travel, speeds, or stationarity of the objects, whether the autonomous driving vehicle can safely move in a predetermined direction. Furthermore, one or more control signals is sent to the autonomous driving vehicle to cause the autonomous driving vehicle to move or to remain stationary based on determining whether the autonomous driving vehicle can safely move in the predetermined direction.

Abstract: Techniques are described for compensating for movements of sensors. A method includes receiving two sets of sensor data from two sets of sensors, where a first set of sensors are located on a roof of a cab of a semi-trailer truck and a second set of sensor data are located on a hood of the semi-trailer truck. The method also receives from a height sensor a measured value indicative of a height of the rear of a rear portion of the cab of the semi-trailer truck relative to a chassis of the semi-trailer truck, determines two correction values, one for each of the two sets of sensor data, and compensates for the movement of the two sets of sensors by generating two sets of compensated sensor data. The two sets of compensated sensor data are generated by adjusting the two sets of sensor data based on the two correction values.

Abstract: Techniques are described for determining an occurrence of theft in a vehicle. An example processor implemented method comprises receiving, by a computer located in an autonomous vehicle and at a first time, a first torque value that indicates a first amount of torque applied by an engine of the autonomous vehicle to drive the autonomous vehicle, receiving, at a second time that is later in time than the first time, a second torque value that indicates a second amount of torque applied by the engine of the autonomous vehicle, determining that a difference between a first value and a second value is greater than a pre-determined value, where the first value and the second value are functions of at least the first torque value and at least the second value respectively, and displaying, in response to the determining, a message that indicates a theft detection in the autonomous vehicle.

Abstract: Techniques for performing multi-sensor calibration on a vehicle are described. A method includes obtaining, from each of at least two sensors located on a vehicle, sensor data item of a road comprising a lane marker, extracting, from each sensor data item, a location information of the lane marker, and calculating extrinsic parameters of the at least two sensors based on determining a difference between the location information of the lane marker from each sensor data item and a previously stored location information of the lane marker.

Abstract: A prediction-based system and method for trajectory planning of autonomous vehicles are disclosed.

Abstract: A redundant hardware and software architecture can be designed to enable vehicles to be operated in an autonomous mode while improving the reliability and/or safety of such vehicles. A system for redundant architecture can include a set of at least two redundant sensors coupled to a vehicle and configured to provide timestamped sensor data to each of a plurality of computing unit (CU) computers. The CU computers can process the sensor data simultaneously based on at least a time value indicative of an absolute time or a relative time and based on the timestamped sensor data. The CU computers provide to a vehicle control unit (VCU) computer at least two sets of outputs configured to instruct a plurality of devices in a vehicle and cause the vehicle to be driven.

Abstract: Devices, systems, and methods for redundant braking systems and architectures are described. An example method for controlling a vehicle includes receiving, by a braking system, a first set of commands generated by a primary brake controller and a primary vehicle control unit (VCU) comprising multiple processors, receiving a second set of commands generated by the primary VCU and a secondary brake controller, receiving a third set of commands generated by a secondary VCU and the primary brake controller, receiving a fourth set of commands generated by the secondary VCU and the secondary brake controller, and selecting, based on an arbitration logic, exactly one of the first, second, third, and fourth sets of commands to operate the braking system, wherein the primary VCU and the secondary VCU are configured in a master/slave architecture.

Abstract: An autonomous vehicle includes a detection system for identifying the presence of authorities or emergency vehicles on a road, such as law enforcement vehicles, fire engines, fire trucks, police officers, first responders, traffic cones, flares, etc. The detection system may include audio and visual detection system dedicated to the detection of authorities. Additionally, systems that correlate the detected authorities and/or any audible or visual instructions given by the authorities to changes in the behavior of the autonomous vehicle. The autonomous vehicle may react to the detected authorities or emergency vehicles by determining a driving related operation to safely operate around the detected authorities or emergency vehicles.

Abstract: A system and method for autonomous vehicle control to minimize energy cost are disclosed. A particular embodiment includes: generating a plurality of potential routings and related vehicle motion control operations for an autonomous vehicle to cause the autonomous vehicle to transit from a current position to a desired destination; generating predicted energy consumption rates for each of the potential routings and related vehicle motion control operations using a vehicle energy consumption model; scoring each of the plurality of potential routings and related vehicle motion control operations based on the corresponding predicted energy consumption rates; selecting one of the plurality of potential routings and related vehicle motion control operations having a score within an acceptable range; and outputting a vehicle motion control output representing the selected one of the plurality of potential routings and related vehicle motion control operations.

Abstract: A system and method for real world autonomous vehicle perception simulation are disclosed. A particular embodiment includes: configuring a sensor noise modeling module to produce simulated sensor errors or noise data with a configured degree, extent, and timing of simulated sensor errors or noise based on a set of modifiable parameters; using the simulated sensor errors or noise data to generate simulated perception data by simulating errors related to constraints of one or more of a plurality of sensors, and by simulating noise in data provided by a sensor processing module corresponding to one or more of the plurality of sensors; and providing the simulated perception data to a motion planning system for the autonomous vehicle.

Abstract: A method of lane detection for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs include instructions, which when executed by a computing device, cause the computing device to perform the following steps comprising: generating a ground truth associated with lane markings expressed in god's view; receiving features from at least one of a hit-map image and a fitted lane marking, wherein the hit-map image includes a classification of pixels that hit a lane marking, and the fitted lane marking includes pixels optimized based on the hit-map image; and training a confidence module based on the features and the ground truth, the confidence module configured to determine on-line whether a fitted lane marking is reasonable, using parameters that express a lane marking in an arc.

Abstract: Disclosed are devices, systems and methods for capturing an image. In one aspect an electronic camera apparatus includes an image sensor with a plurality of pixel regions. The apparatus further includes an exposure controller. The exposure controller determines, for each of the plurality of pixel regions, a corresponding exposure duration and a corresponding exposure start time. Each pixel region begins to integrate incident light starting at the corresponding exposure start time and continues to integrate light for the corresponding exposure duration. In some example embodiments, at least two of the corresponding exposure durations or at least two of the corresponding exposure start times are different in the image.

Abstract: A system comprises a headlight mounted on an autonomous vehicle. The headlight is configured to illuminate at least a portion of a road the autonomous vehicle is on. The system further comprises a control device associated with the autonomous vehicle. The processor obtains information about an environment around the autonomous vehicle. The processor determines that at least a portion of the road should be illuminated if the information indicates that an illumination level of the portion of the road is less than a threshold illumination level. The processor adjusts the headlight to illuminate at least the portion of the road in response to determining that at least the portion of the road should be illuminated.