Abstract: The invention relates to an automated driving control system, including: a data exchange unit configured to obtain video data streams and distribute the video data streams to at least one computing unit; the at least one computing unit configured to compute perception result data based on the video data streams; a sensor fusion unit configured to fuse the perception result data and sensor data, to obtain fusion result data; and a planning control unit configured to generate a driving control instruction based on the fusion result data, where the planning control unit or the sensor fusion unit is configured to provide a bypass of the data exchange unit. When anomalies occur in any of the units, the automated driving control system may provide a corresponding bypass to keep an automated driving function going.

Abstract: A braking method for a vehicle is provided. The method includes the following steps: obtaining a first state information of the vehicle, where the first state information includes a vehicle mass and a deceleration required by braking; calculating a braking torque required by the vehicle according to the first state information, and controlling an output of an electric braking torque according to the braking torque required by the vehicle; obtaining a current vehicle speed of the vehicle and a maximum electric braking exit speed; and; controlling, if the deceleration required by braking of the vehicle changes to zero, the vehicle to unload the electric braking torque when the current vehicle speed is less than the maximum electric braking exit speed. A braking device for a vehicle and a vehicle are further provided.

Abstract: Techniques relating to performance-based metrics for evaluating system quality are described. In an example, sensor data associated with an environment within which a vehicle is positioned is received. A performance metric associated with a trajectory, indicative of a performed behavior of the vehicle, can be determined and a correctness metric can be determined based at least in part on the performance metric. The correctness metric can represent a correctness of the performed behavior. Modification(s) to component(s) and/or system(s), or portions thereof, of the vehicle can be affected based at least in part on the correctness metric.

Abstract: An automated driving vehicle includes: a communication device which receives a traveling plan from an operation management device provided outside an vehicle; and an automated driving controller which allows an vehicle the controller is associated with to travel autonomously along a traveling route on which a plurality of stations are set so as to fulfill the traveling plan, in which the automated driving controller has a standard plan which prescribes a standard operation schedule of the vehicle the controller is associated with, and the automated driving controller, in a case where the traveling plan has not arrived or is irregular, generates an interim plan on the basis of the standard plan, and allows the vehicle the controller is associated with to travel autonomously so as to fulfill the interim plan.

Abstract: Provided is a method of controlling driving of a vehicle using advance information, the method including acquiring preset local information including a road name, a road section, a road attribute, a location of a building, a lane, a traffic signal, and obstacle information for a predetermined region, acquiring a driving experience value resulting from a previous drive using the local information, and setting a target speed corresponding to the road attribute using the driving experience value, determining a driving state and a driving speed of the vehicle on the basis of the local information, and the target speed, of a current position of the vehicle, and generating a driving control command corresponding to the driving state and the driving speed.

Abstract: A driver assistance system (DAS) comprises camera installed on a vehicle, having a field of view in front of the vehicle, and configured to acquire image data; and a controller including a processor configured to process the image data. The controller may be configured to identify at least one object obstructing a driving of the vehicle based on the image data, identify a stopped vehicle among the at least one object based on the image data, and set a driving route of the vehicle based on the position of the at least one object and the position of the stopped vehicle, and controls at least one of a braking device and a steering device of the vehicle so that the vehicle travels along the driving route.

Abstract: Systems, methods, and computer-readable media are disclosed for controlling one or more vehicles with the use of navigation markers positioned or integrated into a ground surface. A vehicle, such as an autonomous vehicle, may include a light detection assembly, which may include a light emitter, an optical filter, an optical sensor, and an analog-to-digital converter, and optionally may include a lens. The light emitter may emit light towards the ground surface which may illuminate the navigation marker and cause the navigation marker to emit light passes through the optical filter and is ultimately sensed by the optical sensor. The vehicle may determine the light was emitted by the navigation marker and cause the vehicle to perform the predetermined action.

Abstract: In a vehicle control device for an autonomous driving vehicle that autonomously travels based on an operation command, a gesture image of a person around the autonomous driving vehicle is acquired, and a stored reference gesture image is collated with the acquired gesture image. At this time, when it is discriminated that the gesture of the person around the autonomous driving vehicle is a gesture requesting the autonomous driving vehicle to stop, it is determined whether a disaster has occurred. When it is determined that the disaster has occurred, the autonomous driving vehicle is caused to stop around the person requesting the autonomous driving vehicle to stop.

Abstract: As a farming machine travels through a field of plants, the farming machine operates in a normal operational state to perform one or more farming operations. The farming machine detects an operational failure of a component of the farming machine using measurements obtained from one or more sensors coupled to and monitoring the farming machine. The operational failure of the component impacts performance of a first farming operation of the farming operations. The farming machine configures the farming machine to operate in a remedial operational state. In the remedial operational state, the farming machine diagnoses the operational failure of the component using the obtained measurements. In the remedial operational state, the farming machine selects a solution operation to address the operational failure of the component based on the diagnosis. The farming machine performs the determined solution operation.

Abstract: Disclosed herein an autonomous driving system includes a first sensor installed in a vehicle, having a field of view facing in front of the vehicle, and configured to acquire front image data; a second sensor selected from the group consisting of a radar and a light detection and ranging, installed in the vehicle, having a detection field of view facing in front of the vehicle, and configured to acquire forward detection data; a communicator configured to receive a high definition map at a current location of the vehicle from an external server; and a controller including a processor configured to process the high definition map, the front image data, and the forward detection data.

Abstract: In a vehicle including a shift-by-wire (SBW) transmission which may be parked in the neutral gear position of the transmission, method for controlling parking thereof includes determining whether the vehicle is in a situation in which neutral parking is required according to sensor information, when the vehicle is stopped, outputting first information configured to get a confirmation on whether a driver intends to perform the neutral parking from the driver, when the controller concludes that the vehicle is in the situation in which the neutral parking is required and when a park (P) gear position is input or an ignition of the vehicle is turned off, and controlling the SBW transmission to shift to a neutral (N) gear position, when there is an agreement input as a response to the first information.

Abstract: A cleaning route determination system includes an analyzer that analyzes behavior of airflow and particles inside a facility, a map generator that generates a dust accumulation map indicating one or more dust accumulation areas inside the facility and one or more dust amounts corresponding to the one or more dust accumulation areas, and a route calculator that determines a first route from second routes. Each of the second routes is a route for a cleaner to pass through, within a certain period of time, at least one of the one or more dust accumulation areas. A total amount indicating a sum of dust amounts corresponding to dust accumulation areas included the first route is largest among total amounts corresponding to the second routes, each of the total amounts indicating a sum of dust amounts corresponding to dust accumulation areas included in each of the second routes.

Abstract: A vehicle control system is configured to group objects detected by a plurality of sensors. The vehicle control system includes an integration device that groups detection information from the plurality of sensors and outputs integrated detection information, and a vehicle control device that controls a vehicle on the basis of the integrated detection information.

Abstract: Methods for planning a trajectory for an autonomous vehicle are disclosed. A vehicle motion planning system will determine a reference curve that represents a path via which the vehicle may travel. The system will detect an actor that is moving in the environment. The system will segment the reference curve according to time intervals. For each of the time intervals, the system will identify a bounding geometry for the actor, predict a lateral offset distance between the reference curve and the actor, and use the predicted lateral offset distance to determine whether to alter a planned trajectory for the autonomous vehicle.

Abstract: A system for avoiding blind spot using accident history information, includes an image sensor configured to provide image information by acquiring a surrounding image of a host vehicle, and a vehicle controller. The vehicle controller is configured to detect, through the image sensor, an adjacent vehicle traveling adjacent to the host vehicle and a license plate of the adjacent vehicle; determine a dangerous level of the blind spot of the adjacent vehicle, based on accident history information of the adjacent vehicle and driver tendency information of a driver of the adjacent vehicle obtained by inquiring about the license plate of the adjacent vehicle, after determining a blind spot range of the adjacent vehicle; and generate a path in which the host vehicle deviates from the blind spot or avoids the blind spot to reduce the dangerous level of the blind spot, based on a traveling situation of the host vehicle.

Abstract: The systems and methods disclosed herein are configured to determine if a trailer is connected to a vehicle. The methods may include determining a connection during an off-state and an on-state of the vehicle. The determinations may be compared to determine whether to enable use of the off-state determination with respect to features of the vehicle.

Abstract: An electronic device is disclosed. The electronic device comprises a communication interface, a memory in which a program for performing an autonomous driving function is stored, and a processor which performs second processing for first processed data on the basis of a first program stored in the memory, when the first processed data is received from an external sensor device through the communication interface, and which performs, on the basis of a second program stored in the memory, first processing for raw data received from the external sensor device, and then performs the second processing on the basis of the first program, when the occurrence of an error in the reception of the data is identified.

Abstract: Methods and systems for improved simultaneous localization and mapping based on 3-D LIDAR image data are presented herein. In one aspect, LIDAR image frames are segmented and clustered before feature detection to improve computational efficiency while maintaining both mapping and localization accuracy. Segmentation involves removing redundant data before feature extraction. Clustering involves grouping pixels associated with similar objects together before feature extraction. In another aspect, features are extracted from LIDAR image frames based on a measured optical property associated with each measured point. The pools of feature points comprise a low resolution feature map associated with each image frame. Low resolution feature maps are aggregated over time to generate high resolution feature maps.

Abstract: Techniques for determining error models for use in simulations are discussed herein. Ground truth perception data and vehicle perception data can be determined from vehicle log data. Further, objects in the log data can be identified as relevant objects by signals output by a planner system or based on the object being located in a driving corridor. Differences between the ground truth perception data and the vehicle perception data can be determined and used to generate error models for the relevant objects. The error models can be applied to objects during simulation to increase realism and test vehicle components.

Abstract: In various examples, systems and methods are disclosed that preserve rich, detail-centric information from a real-world image by augmenting the real-world image with simulated objects to train a machine learning model to detect objects in an input image. The machine learning model may be trained, in deployment, to detect objects and determine bounding shapes to encapsulate detected objects. The machine learning model may further be trained to determine the type of road object encountered, calculate hazard ratings, and calculate confidence percentages. In deployment, detection of a road object, determination of a corresponding bounding shape, identification of road object type, and/or calculation of a hazard rating by the machine learning model may be used as an aid for determining next steps regarding the surrounding environment—e.g., navigating around the road debris, driving over the road debris, or coming to a complete stop—in a variety of autonomous machine applications.