Abstract: Disclosed are devices, systems and methods for curb detection using light detection and ranging (LiDAR) sensors. One example of a method for curb detection includes acquiring, based on a scan of an area around a vehicle, a point-cloud frame that includes a description of an intensity of a reflection of the area around the vehicle, selecting a subset of points from the point-cloud frame, seeding each of a plurality of clusters based on the selected points, determining a criterion for each of the selected points, increasing a size of each of the plurality of clusters by including additional points from the point-cloud frame that meet the criterion for the corresponding cluster, identifying a largest cluster of the plurality of clusters upon completion of the increasing step, and detecting a curb based on a boundary of the largest cluster.

Abstract: A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed. A particular embodiment includes: partitioning a multiple agent autonomous vehicle control module for an autonomous vehicle into a plurality of subsystem agents, the plurality of subsystem agents including a deep computing vehicle control subsystem and a fast response vehicle control subsystem; receiving a task request from a vehicle subsystem; dispatching the task request to the deep computing vehicle control subsystem or the fast response vehicle control subsystem based on the content of the task request or a context of the autonomous vehicle; causing execution of the deep computing vehicle control subsystem or the fast response vehicle control subsystem by use of a data processor to produce a vehicle control output; and providing the vehicle control output to a vehicle control subsystem of the autonomous vehicle.

Abstract: Disclosed is a camera mounting apparatus for a vehicle that attaches the camera to the vehicle, isolates the camera from vibration at the mounting points, and accommodates thermal expansion and contraction of the vehicle and the mounting structure. The apparatus includes a camera mounting beam coupled to the camera, and one or more elastomeric vibration isolators coupled to an attachment device and coupled to the camera mounting beam. The apparatus also includes a spherical bearing coupled to the attachment device, wherein the spherical bearing is configured to accommodate misalignment of the attachment device, and one or more spring washers to generate a force holding the one or more elastomeric vibration isolators and spherical bearing in positions.

Abstract: Image processing techniques are described to receive bounding box information that describes a bounding box located around a detected object in an image, determine one or more positions of one or more reference points on the bounding box, determine, for each reference point, 3D world coordinates of a point of intersection of the reference point and the road surface, and assign the 3D world coordinates of the one or more reference points to a location of the detected object.

Abstract: A lead autonomous vehicle (AV) includes a sensor configured to observe a field of view in front of the lead AV. Following AVs are on the same road behind the lead AV. A processor of the lead AV is configured to detect a road closure. The processor overrides driving instructions of the lead AV, such that the lead AV is stopped at first location coordinates. The processor sends a first message to an operation server, indicating that the road closure is detected. The operation server update the first portion of the map data, reflecting the road closure. The operation server determines whether re-routing is possible for each AV. If re-routing is possible is possible for that AV, the operation server sends re-routing instructions to the AV. If re-routing is not possible is possible for that AV, the operation server sends pulling over instructions to the AV.

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 generates a ticket for the unexpected ticket. The operation server communicates the particular information to the user. The operation server provides instructions to be forwarded to the user based on a request from the user. The operation server closes the ticket if the unexpected event is addressed.

Abstract: Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes receiving two sets of values from two sets of sensors, where a first set of sensors measure weights or pressures applied on axles of a vehicle, and where a second set of sensors measure pressures in tires of the vehicle. The method performs an error detection and removal operation to remove or filter out any erroneous values from the two sets of values to obtain two sets of filtered values. The method determines one or more values that describe a weight or pressure applied on the axle to obtain the weight distribution of the vehicle based on the first set of filtered values or the second set of filtered values. Based on the obtained weight distribution of the vehicle, the method can determine a driving operation of the vehicle.

Abstract: A system and method for real world autonomous vehicle trajectory simulation may include: receiving training data from a data collection system; obtaining ground truth data corresponding to the training data; performing a training phase to train a plurality of trajectory prediction models; and performing a simulation or operational phase to generate a vicinal scenario for each simulated vehicle in an iteration of a simulation. Vicinal scenarios may correspond to different locations, traffic patterns, or environmental conditions being simulated. Vehicle intention data corresponding to a data representation of various types of simulated vehicle or driver intentions.

Abstract: An autonomous vehicle (AV) includes at least one vehicle sensor that is configured, when the AV is stopped, to observe at least a first portion of a zone around the stopped AV. A portable device is operated by a user near the stopped AV. A control subsystem receives a communication from the portable device that includes information regarding whether a second portion of the zone around the AV is free of obstructions. The control subsystem receives AV sensor data from the at least one vehicle sensor. The control subsystem determines whether the first portion of the zone free of obstructions and whether the second portion of the zone is free of obstructions. If that both the first and second portions of the zone around the stopped AV are free of obstructions, the stopped AV is allowed to begin moving.

Abstract: Systems and methods for dynamic predictive control of autonomous vehicles are disclosed. In one aspect, an in-vehicle control system for a semi-truck includes one or more control mechanisms configured to control movement of the semi-truck and a processor. The system further includes computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive a desired trajectory and a vehicle status of the semi-truck, determine a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, determine at least one quadratic program (QP) problem based on the dynamic model, generate at least one control command for controlling the semi-truck by solving the at least one QP problem, and provide the at least one control command to the one or more control mechanisms.

Abstract: Image processing techniques are described to select and crop a region of interest from an image obtained from a camera located on or in a vehicle, such as an autonomous semi-trailer truck. The region of interest can be identified by selecting one or more reference points and determining one or more positions of the one or more reference points on the image obtained from the camera. As an example, a location of two reference points may be 500 meters and 1000 meters in front of a location of autonomous vehicle, where the front of the autonomous vehicle is an area towards which the autonomous vehicle is being driven.

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: 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: Technique for performing camera calibration on a vehicle is disclosed. A method of performing camera calibration includes emitting, by a laser emitter located on a vehicle and pointed towards a road, a first laser pulse group towards a first location on a road and a second laser pulse group towards a second location on the road, where each laser pulse group includes one or more laser spots. For each laser pulse group: a first set of distances are calculated from a location of a laser receiver to the one or more laser spots, and a second set of distances are determined from an image obtained from a camera, where the second set of distances are from a location of the camera to the one or more laser spots. The method also includes determining two camera calibration parameters of the camera by solving two equations.

Abstract: Power distribution system architectures are described that can safely and effectively support the power needs of automotive original equipment manufacturer (OEM) systems and state-of-the-art autonomous systems and devices. In some implementations, a power distribution system may include: vehicle power sources that produce an output voltage to operate one or more devices in the vehicle, and power bridge devices that electrically couple a vehicle power source to the multiple banks of battery bridge devices and to power distribution units (PDUs). Various electrical loads in the vehicle can be electrically coupled to the battery bridge devices and to the PDUs.

Abstract: A system and method for taillight signal recognition using a convolutional neural network is disclosed. An example embodiment includes: receiving a plurality of image frames from one or more image-generating devices of an autonomous vehicle; using a single-frame taillight illumination status annotation dataset and a single-frame taillight mask dataset to recognize a taillight illumination status of a proximate vehicle identified in an image frame of the plurality of image frames, the single-frame taillight illumination status annotation dataset including one or more taillight illumination status conditions of a right or left vehicle taillight signal, the single-frame taillight mask dataset including annotations to isolate a taillight region of a vehicle; and using a multi-frame taillight illumination status dataset to recognize a taillight illumination status of the proximate vehicle in multiple image frames of the plurality of image frames, the multiple image frames being in temporal succession.

Abstract: Described are devices, systems and methods for real-time remote control of vehicles with high redundancy. In some embodiments, two copies of at least one control command are received using two different wireless communication protocols, and are compared. The at least one control command is executed when the two copies are in agreement, but is rejected when the two copies differ. In other embodiments, additional wireless communication protocols may exist to provide a redundant mode of communication when one of the two different wireless communication protocols are unavailable. In yet other embodiments, redundant GPS units may be used to determine availability of any of the communication protocols, and relevant control commands may be downloaded in advance to circumvent the lack of coverage.

Abstract: Disclosed are devices, systems and methods for a monitoring system for autonomous vehicle operation. In some embodiments, a vehicle may perform self-tests, generate a report based on the results, and transmit it to a remote monitor center over one or both of a high-speed channel for regular data transfers or a reliable channel for emergency situations. In other embodiments, the remote monitor center may determine that immediate intervention is required, and may transmit a control command with high priority, which when received by the vehicle, is implemented and overrides any local commands being processed. In yet other embodiments, the control command with high priority is selected from a small group of predetermined control commands the remote monitor center may issue.

Abstract: A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.