Abstract: Systems and techniques are provided for providing a preorder delivery in an autonomous vehicle (AV). An example method includes receiving, from a computing device associated with a user, a first user request for a ride in an AV from a user location to a user destination; receiving, from the computing device associated with the user, a second user request for the AV to pick up a product at a pick up location associated with the product, prior to picking up the user; determining, based on the first and second user requests, a route for the AV to navigate; and sending, from a computer associated with the AV to a locker of the AV, an instruction to open the locker where the product is placed.

Abstract: Systems and techniques are provided for communicating using an automotive audio bus. An example method can include receiving, by a first automotive audio bus manager, a request from a first automotive audio bus client for a first data transmission to a second automotive audio bus client, wherein a first size of the first data transmission exceeds a maximum data frame size; parsing the first data transmission into a first plurality of data frames based on the first size of the first data transmission and the maximum data frame size; sending a first header frame to a second automotive audio bus manager associated with the second automotive audio bus client, wherein the first header frame includes a first indication of a first incoming long message transmission and the first size of the first data transmission; and sending the first plurality of data frames to the second automotive audio bus manager.

Abstract: The subject disclosure relates to features that improve safety for autonomous vehicle (AV) driving maneuvers. In some aspects, a process of the disclosed technology includes steps for detecting an unprotected maneuver, navigating an AV into an intersection, and detecting a wheel safety angle relative to the intersection. In some aspects, the process further includes steps for automatically adjusting a wheel angle of the AV based on the wheel safety angle. Systems and machine-readable media are also provided.

Abstract: Systems and techniques are described for Light Detection and Ranging (LiDAR) noise modeling using machine learning (ML). An example method can include collecting, using one or more sensors, a first set of data for a simulation environment and generating, using the first set of data, a point cloud that represents and/or describes the simulation environment. The method can further include collecting, using the one or more sensors, a second set of data for a real-world environment and generating a noise model using the second set of data and a neural network. The method can also include generating, using the noise model and the point cloud, a noisy point cloud that represents and/or describes the real-world environment.

Abstract: A system for filtering RADAR data includes one or more graphical processing units (GPUs) for performing steps of a filtering process in parallel. For example, a first GPU or first portion of GPU circuitry calculates a threshold parabola for a tensor of RADAR data. In parallel, a second GPU or second portion of GPU circuitry separately reduces and indexes the tensor for comparison to the threshold parabola. The threshold parabola is compared to reduced and indexed data to filter the RADAR data. Importance sampling can also be used to reduce data, e.g., if the RADAR data includes four dimensions (range, Doppler, azimuth, and elevation).

Abstract: The present technology is directed to determining an accuracy of an infrared (IR) camera, and more particularly, validating a distortion accuracy of an IR camera. The present technology can receive one or more images of a calibration harp captured by the IR camera, wherein the one or more images include a plurality of lines corresponding to a plurality of strings of the calibration harp. The present technology can further determine a degree of distortion of the plurality of lines based on a distortion error coefficient, wherein the distortion error coefficient is computed based on edge points of the plurality of lines on the one or more images.

Abstract: System, methods, and computer-readable media for a calibration check process of sensors on an autonomous vehicle (AV) via a drive-through environment mapped with a series of targets. The targets are used to check the calibration of the sensors on the AV as a quality check before the AV is determined fit to drive autonomously. Supervising a calibration process that collects data from the sensors on the AV based on exposure to a series of targets or augmented camera targets. The collected data may be used for extrinsic calibration aimed to determine that extrinsic parameters that define the rigid relationship between sensors are in set checker bounds.

Abstract: A vehicle (AV) includes a radar sensor and a hardware logic component. The radar sensor receives a radar return from a driving environment of the vehicle and outputs radar data that is indicative of the return to the hardware logic component. The hardware logic component further receives data indicative of a velocity of the vehicle from a sensor mounted on the vehicle. The hardware logic component is configured to employ synthetic aperture radar (SAR) techniques to compute a three-dimensional position of a point on a surface of an object in the driving environment of the vehicle based upon the radar data and the velocity of the vehicle.

Abstract: A method for performing direct logging of sensor data in a vehicle network logging system (VNLS) installed on an autonomous vehicle (AV), the VNLS comprising a network switch connected between an onboard sensor of the AV and an onboard computer of the AV, the network switch comprising a logger for logging sensor data to a disk, is described and includes providing a converter node on the network switch; receiving, using the converter node, data generated by the onboard sensor; and converting, using the converter node, the received data to a data record suitable for logging by the logger to the disk.

Abstract: Disclosed are embodiments for facilitating a system-level optimization route and mode suggestion platform for transportation trips. In some aspects, an embodiment includes receiving identification of an origin (O) location and a destination (D) location of an O/D pair corresponding to a transportation trip request of a user; determining N pickup (PU) locations for the O location and N drop-off (DO) locations for the D location; determining an origin walk duration between each N PU locations and the O location and a destination walk duration between each N DO locations and the D location; routing between the N PU locations and the N DO locations to determine an estimate of in-vehicle time for each route; and selecting a route that minimizes a route score, wherein the route score is based on a sum of the origin walk duration, the in-vehicle time, and the destination walk duration.

Abstract: Assistance can be provided to users of AVs based on user sentiments. A system may receive a request for assistance from a user of an AV (e.g., a passenger of the AV). The system may also receive sensor data that is captured by a sensor suite of the AV from detecting the user, the AV, or another object. The system determines a sentiment of the user based on the sensor data. The system may input the sensor data into a machine learning model and the model outputs information indicating the user's sentiment. The system can determine who, when, or how to service the user's request based on the user's sentiment. The system may assign the user's request to an agent who can provide the assistance. The system can further provide guidance (e.g., suggested content of a conversation with the user) to the agent based on the user's sentiment.

Abstract: The disclosed technology provides solutions for providing an accurate pick-up location for an autonomous vehicle and a rider during a ride hailing pick-up. A method of the disclosed technology can include steps for determining map information associated with at least one thoroughfare for routing an autonomous vehicle, wherein the map information is based on location data associated with a client device requesting the autonomous vehicle; receiving sensor data from the client device corresponding to the at least one thoroughfare; determining, based on the sensor data and the map information, a first position of the client device relative to the at least one thoroughfare; and routing the autonomous vehicle based on the first position of the client device relative to the at least one thoroughfare. Systems and machine-readable media are also provided.

Abstract: Systems and methods are provided for autonomous vehicle recovery. In particular, systems and methods are provided for vehicle recovery prioritization in a fleet of vehicles when multiple vehicles request recovery. Stranded vehicles in need of recovery assigned a risk profile and prioritized based on the risk profile. The risk profile can be based on a number of factors, such as a risk level for the vehicle due to the vehicle's situation, the presence of passengers in the vehicle, and congestion caused by the vehicle. Recovery response can be based on the recovery prioritization. Vehicle sensors and computing power can be used to inform onboard processors and/or central computers of a risk profile for the vehicle, and dispatch can trigger a response plan according to a recovery response framework.

Abstract: A method of providing an onboard assistant for a vehicle, comprising: receiving sensor data from sensors of the vehicle; transforming the sensor data using a trained machine learning model to provide transformed sensor data; inferring from the transformed sensor data that a special condition for the vehicle requires intervention; and contacting an operator of the vehicle to inform the operator of the special condition and receive instructions for handling the special condition.

Abstract: The disclosed technology provides solutions for improving solar carport systems and in particular, for improving a solar carport integrated with a busbar. Aspects of the disclosed technology include an integrated solar carport that includes at least one beam, at least one rail coupled to the at least one beam, and one or more vertical columns configured to support the at least one beam and the at least one rail. Further, the integrated solar carport includes one or more solar modules coupled to the at least one beam or the at least one rail and a busbar coupled to each beam of the at least one beam and one of the vertical columns. Methods of manufacturing the integrated solar carport are also provided.

Abstract: A method of providing an onboard assistant for a vehicle, comprising: receiving sensor data from sensors of the vehicle; transforming the sensor data using a trained machine learning model to provide transformed sensor data; inferring from the transformed sensor data that a special condition for the vehicle requires intervention; and contacting an operator of the vehicle to inform the operator of the special condition and receive instructions for handling the special condition.

Abstract: There is disclosed a method of operating an autonomous vehicle (AV), including: detecting an alert condition within the AV via data from an electronic sensor suite of the AV; based at least in part on detecting the alert condition, automatically initiating a call with another party; and communicatively coupling a passenger of the AV to the conference call, whereby the passenger can communicate with the other party via the conference call.

Abstract: Autonomous vehicles can offer affordable, accessible, and efficient transportation for passengers and cargo. For passengers with different mobility abilities, it can be a challenge to enter and exit an autonomous vehicle. It may also be difficult to bring a wheeled carrier in and out of an autonomous vehicle. A smart ramp can be included with an autonomous vehicle to improve the experience and safety of those with different mobility abilities, and to make it easier for wheeled carriers to go up and go down the ramp. The smart ramp has actuatable tiles that can be selectively actuated based on the position of a wheel of a wheeled carrier and a direction of travel of the wheeled carrier. The tiles may have a rubber-like material on a top side to provide grip and traction for users of the ramp.