Abstract: A system for capturing location-based data includes an interface and a processor. The interface is configured to receive a location-based data description for capturing the location-based data. The processor is configured to determine a location based at least in part on the location-based data description, create a location-based data identification job based at least in part on the location, cause execution of the location-based data identification job to a set of vehicle event recorder systems, wherein the location-based data identification job is executed by a vehicle event recorder system of the set of vehicle event recorder systems to acquire sensor data in response to a vehicle being able to acquire the sensor data related to the location of the location-based data identification job, receive the sensor data from the vehicle event recorder system, and determine the location-based data based at least in part on the sensor data.

Abstract: Systems and methods for testing a machine learning algorithm or technique of an autonomous driving feature of a vehicle utilize a sensor system configured to capture input data representative of an environment external to the vehicle and a controller configured to receive the input data from the sensor system and perform a testing procedure for the autonomous driving feature that includes inserting known input data into a target portion of the input data to obtain modified input data, processing the modified input data according to the autonomous driving feature to obtain output data, and determining an accuracy of the autonomous driving features based on a comparison between the output data and the known input data.

Abstract: A control device for a vehicle includes an electronic control unit configured to: execute driving assistance control for driving the vehicle at least by automatically controlling a speed irrespective of a driving operation of a driver; execute regenerative braking using a rotator at a time of deceleration during travel under the driving assistance control, and to restrict downshift of an automatic transmission when a demanded deceleration amount for the vehicle is equal to or lower than a predetermined deceleration amount; and execute the regenerative braking using the rotator at the time of deceleration during the travel under the driving assistance control, and not to restrict the downshift when the demanded deceleration amount is higher than the predetermined deceleration amount.

Abstract: A delivery system includes a processor programmed to construct a route so as to include predefined segments traveled by carriers configured to taxi the vehicle and charge a battery thereof such that a state of charge of the battery remains above a target for a duration of the route, and forward the route to the vehicle.

Abstract: Provided herein is a method of generating low bandwidth map format agnostic routes between origins and destinations for route communication between different map formats or versions using reduced bandwidth. Methods may include: receiving an indication of a route request between an origin and a destination; generating a route between the origin starting road segment and the destination target road segment, the route including a plurality of road segments; identifying one or more intermediate segments from the plurality of road segments, each intermediate segment having an anchor point; generating a plurality of route fragments from the route; encoding each route fragment by applying an XOR algorithm to identifiers of route fragment road segments of a respective route fragment; and providing the encoded route fragments and one or more anchor points of the one or more intermediate segments in response to the route request.

Abstract: The invention relates to a method for creating a probabilistic free space map with static (2a, 2b, 3) and dynamic objects (V1-V7), having the following steps: retrieving (S1) static objects (2a, 2b, 3) as well as a perception area polygon (WP) from an existing environment model; collecting (S2) predicted trajectories (T1, T2) of dynamic objects (V1-V7); merging (S3) the static objects (2a, 2b, 3) of the perception area polygon (WP) and the predicted trajectories (T1, T2) in a first free space map; fixing (S4) a maximum prediction time; fixing (S5) prediction time steps; fixing (S6) a current prediction time and setting this current prediction time to the value 0 in order to fix the start of a fixed prediction time period; fixing (S7) confidence regions (K) around the static (2a, 2b, 3) and dynamic objects (V1-V7); fixing (S8) at least one uncertain region (U) around at least one static (2a, 2b, 3) or dynamic object (V1-V7); producing (S9) a first probabilistic free space map for the current prediction time

Abstract: The present disclosure relates to detecting distracted driving on a mobile device and mitigating distracted driving by determining whether a mobile device is used by a driver of a vehicle or by a passenger of the vehicle. The system can determine that the mobile device is in a vehicle and modify the functionality of the mobile device to transition the mobile device into an automobile mode. Vehicle data and/or mobile device data can be used to determine a number of individuals in the vehicle. An identity of the operator of the mobile device can be authenticated based on authentication information obtained by the mobile device. Based on the authenticated identity of the operator of the mobile device, a driving score associated with the driver can be modified, where the score indicates a number of instances of distracted driving by the driver.

Abstract: A computer-implemented method for localizing a vehicle can include accessing, by a computing system comprising one or more computing devices, a machine-learned retrieval model that has been trained using a ground truth dataset comprising a plurality of pre-localized sensor observations. Each of the plurality of pre-localized sensor observations has a predetermined pose value associated with a previously obtained sensor reading representation. The method also includes obtaining, by the computing system, a current sensor reading representation obtained by one or more sensors located at the vehicle. The method also includes inputting, by the computing system, the current sensor reading representation into the machine-learned retrieval model.

Abstract: Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

Abstract: Systems and methods for detecting electromagnetic emissions to monitor and manage road traffic. In one implementation, a system is provided for determining at least one of location, speed, and direction of vehicles on a roadway. The system comprising at least one receiver configured for placement at one or more fixed locations along the roadway to detect a plurality of non-reflected electromagnetic emissions originating from a plurality of vehicles. The system further comprise at least one processor configured to receive signal information from the at least one receiver and to identify in the plurality of non-reflected electromagnetic emissions an electromagnetic waveform of a vehicle. The at least one processor may calculate at least one of a Doppler effect, a phase difference, or a time difference of non-reflected electromagnetic emissions associated with the identified electromagnetic waveform, and determine at least one of a location, speed, and direction of the vehicle.

Abstract: A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Abstract: A vehicle may receive sensor data captured by a sensor, determine that the sensor data represents an object in the environment, and determine a collision probability associated with a collision between the vehicle and the object. Based at least in part on the collision probability, the vehicle may determine one or more mitigating actions to perform prior to, during, and/or after the collision. The mitigating action may be associated with adjusting a hydraulic fluid pressure in at least a portion of a hydraulic fluid system of the vehicle.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a vehicle may comprise at least one processor. The at least one processor may be programmed to obtain a route from a location of the vehicle to a destination; receive signal information indicative of a quality characteristic of a signal associated with at least one location along the route; determine a quality parameter for at least one operational characteristic associated with a communication system; and determine at least one modification to the route for the vehicle based on the signal information and the quality parameter.

Abstract: An object detection method and apparatus for a vehicle in a confined space are provided. An object detection method for a vehicle traveling in a confined space, includes determining a first beam pattern and a second beam pattern based on geometric information of the confined space, detecting first candidate objects based on a first transmission signal emitted to form the first beam pattern using at least one antenna, detecting second candidate objects based on a second transmission signal emitted to form the second beam pattern using the at least one antenna, detecting at least one clutter object based on the first candidate objects and the second candidate objects, and detecting a target object based on the at least one clutter object.

Abstract: A system includes one or more processors and a communication device onboard a first remote vehicle of a vehicle system that includes a controlling vehicle. The one or more processors are configured to establish a communication link with the controlling vehicle, and, in a first mode of operation, to automatically control movement of the designated remote vehicle responsive to movement-control messages received from the controlling vehicle over the communication link. The one or more processors are also configured, responsive to receipt of a designated suspend message from the controlling vehicle, to switch the designated remote vehicle from the first mode of operation to a second mode of operation where movement of the designated remote vehicle is controlled independently of the controlling vehicle while maintaining the communication link.

Abstract: An Aerial Robotics Network (ARN) Management Infrastructure (MI) (also referred to as ARNMI) that provides a mechanism for the management of aerobots.

Abstract: A sensor calibration system periodically receives scene data from a detector in a calibration scene. The calibration scene includes calibration targets. The sensor calibration system generates a calibration map based on the scene data. The calibration map is a virtual representation of the calibration scene and includes features of the calibration targets that can be used as ground truth features for calibrating AV sensors. The sensor calibration system can periodically update the calibration map. For instance, the sensor calibration system receives the scene data at a predetermined frequency and updates the calibration map every time it receives new scene data. The predetermined frequency may be a frequency of the detector completing a full scan of the calibration scene. The sensor calibration system provides a latest version of the calibration map for being used by an AV to calibrate a sensor on the AV 110.

Abstract: A vehicle safety system and technique are described. In one example, a vehicle safety system communicably coupled to a vehicle, comprises processing circuitry coupled to a camera unit and a LiDAR sensor, the processing circuitry to execute logic operative to analyze an image captured by one or more cameras to identify predicted regions of road damage, correlate LiDAR sensor data with the predicted regions of road damage, analyze the LiDAR sensor data correlated with the predicted regions of road damage to identify regions of road damage in three-dimensional space; and output one or more indications of the identified regions of road damage, wherein the processing circuitry is coupled to an interface to the vehicle, the processing circuitry to output an identification of road damage.

Abstract: An automated driving trajectory generating device is configured to: recognize a mobile object which is located near a vehicle; calculate a host vehicle path for automated driving of the vehicle and a plurality of predicted paths of the mobile object based on a position of the vehicle on a map, a position of the mobile object on the map, and map information; calculate a predicted acceleration which is generated in the mobile object moving along a predicted path for each predicted path based on the plurality of predicted paths and a vehicle speed of the mobile object; identify a target path which is a predicted path used to generate the trajectory out of the plurality of predicted paths based on a result of comparison between the predicted acceleration and an acceleration threshold value; and generate the trajectory based on the host vehicle path and the target path.

Abstract: A system for controlling operation of a vehicle includes a millimeter-wave radar sensor, a processor, and a memory communicably coupled to the processor. The memory stores a sensor control module configured to control operation of the radar sensor to acquire data for determining and updating a number of occupants in the vehicle. Upon receiving a control command whose execution will result in movement of the vehicle, a pre-movement radar scan of the vehicle interior is performed. Using information acquired by the pre-movement scan, the sensor control module determines if an excessive occupant condition exists in the vehicle interior. Responsive to a determination that an excessive occupant condition exists, the sensor control module may control an operation of the vehicle other than executing the control command.