Abstract: The invention provides a system (10) for a motor vehicle (100) that receives drive demand information (161S) indicative of an amount of drive demanded of a powertrain (129) of the vehicle (100), and controls an amount of drive torque applied by the powertrain (129) to one or more road wheels (111, 112, 114, 115) in dependence on the drive demand information (116S). The system also receives gradient information (11GS) relating to the driving surface and vehicle speed information (Sv). The control system, in dependence on the gradient and speed information, automatically causes a braking system (12d) to apply brake force to one or more of the wheels (111, 112, 114, 115) to prevent vehicle rollback, and adjusts the amount of brake force applied in dependence on the drive demand information (161S) to cause the amount of brake force applied to increase progressively as the amount of drive demand decreases.

Abstract: Embodiments provide a vehicle computer coupled to a vehicle. The vehicle computer may be configured to compute (e.g., generate) a first (e.g., regular, main) trajectory and a second (e.g., fail-safe, minimal risk maneuver) trajectory for the vehicle. Embodiments may provide a fault detection interval during which the second trajectory may be activated due to missing updated trajectory input. Embodiments may also provide a recovery detection interval during which an updated regular trajectory may be generated and followed instead of the fail-safe trajectory if a valid trajectory update is received. Accordingly, embodiments allow for an early activation of a fail-safe trajectory compared to conventional systems, while also allowing the system to recover (e.g., revert back to a modified version of the first trajectory) if a valid trajectory update is received during a recovery detection interval.

Abstract: A computer implemented method is provided for generating a dynamic occupancy grid in front of a host vehicle. An indicator is detected for the course of a lane of a road in front of the host vehicle, and a base area is determined based on the detected indicator, wherein the base area is restricted to a region of interest in front of the host vehicle. A plurality of cells is defined by dividing the base area in order to form the occupancy grid. For each cell of the occupancy grid, it is determined whether the cell is occupied at least partly by a detectable object.

Abstract: A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.

Abstract: In one embodiment, a perception module of an autonomous driving vehicle (ADV) detects a temporary traffic control device (TTCD) located within a first lane of a multi-lane roadway. The first lane is added to a black list of one or more lanes that the ADV is not permitted to drive within. A rerouting request is made to a planning module of the ADV to route the ADV to a second lane in the multi-lane roadway. The ADV navigates to the second lane and continues navigating along the requested rerouting. The ADV monitors for additional TTCDs. One or more boundary lines of the first lane can be marked “do not cross” so that the ADV does not navigate, even partially, back into the first lane. If there are no more TTCDs in the first lane for a predetermined distance ahead of the ADV, the first lane is deleted from the black list.

Abstract: Embodiments is disclosed to detect a locked heading direction of a pedestrian and to predict a path for the pedestrian using the locked heading direction. According to one embodiment, a system perceives an environment of an autonomous driving vehicle (ADV) using one or more image capturing devices. The system detects a pedestrian in the perceived environment. The system determines a facing direction of the pedestrian relative to the ADV as one of left/right side, front, or back. If the facing direction of the pedestrian is determined to be front or back facing, the system determines a lane nearest to the pedestrian. The system projects the pedestrian onto the nearest lane to determine a lane direction at the projection. The system determines a heading direction for the pedestrian locking to the lane direction of the nearest lane based on a predetermined condition.

Abstract: A management system of an automated valet parking lot according to an example in the present disclosure is a management system of an automated valet parking lot attached to a facility. The management system includes a setting unit for setting a management area for the facility, an authentication unit for performing, when a vehicle is deposited in the automated valet parking lot, usage authentication of the automated valet parking lot with a portable terminal associated with the vehicle, a tracking unit for tracking the position of the portable terminal authenticated for the usage, and a notification unit for notifying the portable terminal of an alarm when the portable terminal is outside the management area.

Abstract: An apparatus, or corresponding method, for building or updating a map database is described. In one example, the apparatus includes an image correlation module, a training device, and a learned model or neural network. The image correlation module is configured to correlate a first aerial image and terrestrial sensor data collected at a terrestrial vehicle based on at least one control point from the terrestrial data. The learned model training device is configured to define a learned model based using at least one control point from the terrestrial sensor data as ground truth for analysis of the first aerial image. The learned model inference module is configured to receive a second aerial image and apply the learned model on the second aerial image for identification of mapping information for the map data.

Abstract: Methods and systems for navigating a vehicle. The system includes an input/output device of the vehicle configured to receive a destination from a user of the vehicle. The system also includes a transceiver of the vehicle configured to receive traffic data and traffic light timing data of traffic lights between a current location of the vehicle and the destination. The system also includes an electronic control unit (ECU) of the vehicle configured to determine one or more routes from the current location to the destination based on the traffic data and the traffic light timing data.

Abstract: A number of illustrative variations may include a method of communication between a driver and an autonomous steering system.

Abstract: The present disclosure relates to a method for the automatic transverse guidance of a following vehicle in a vehicle platoon. The method comprises determining and receiving a plurality of lane data. A lane course is able to be determined on the basis of the plurality of lane data and the following vehicle is able to be transversely guided on the basis of the determined lane course. The method is able to prevent an increase in swaying movements from the front to the rear in the vehicle platoon. In addition, the vehicles of the vehicle platoon are able to remain in the starting lane, even when a vehicle of the vehicle platform driving in front changes lane.

Abstract: A driving lane keeping control system includes a number of vehicles and a server. Each vehicle is configured to transmit wheel position information to the server. The server is configured to receive the wheel position information from each vehicle, to match the wheel position information with a detailed map to generate a moving trajectory of each vehicle, to analyze the generated moving trajectory of each vehicle to select an optimum moving trajectory, to generate virtual lane information based on the selected optimum moving trajectory, and to transmit the generated virtual lane information to at least one of the vehicles to enable the at least one vehicle to correct a driving position based on the virtual lane information.

Abstract: Systems and methods for training a neural network for estimating a trajectory of a vehicle are disclosed. In one embodiment, a system includes one or more processors, and a non-transitory computer-readable medium storing computer-readable instructions. The computer-readable instructions cause the one or more processors to receive sensor data of a plurality of examples from a plurality of vehicle sensors, input the sensor data into a sensor data neural network to generate a sensor data intermediate space and receive structured data of the plurality of examples. The computer-readable instructions cause the one or more processors to input the structured data into a structured data neural network to generate a structured data intermediate space, calculate a first loss between the sensor data intermediate space and the structured data intermediate space using a first loss function, and provide the first loss to the sensor data neural network and the structured data neural network.

Abstract: Embodiments of this application provide a method and an apparatus for determining drivable region information. The method includes obtaining first information, where the first information includes information about an initial drivable region determined based on at least one image, and the at least one image is from at least one camera module. The method also includes obtaining second information, where the second information includes radar detection information. The method further includes determining first drivable region information based on the first information and the second information.

Abstract: A robotically controlled system including a fence enclosure that can move and/or shape-shift itself autonomously and automatically without manual intervention. The system includes coils of electric fence wire that are spring-loaded or otherwise tensioned so that the length of electric fence wire between each post robot is able to contract or expand as the system of robots adapts to optimize the shape of the fence enclosure as the entire enclosure is moved by the robots around a field or other grazing pasture on an area of land.

Abstract: This document describes techniques, apparatuses, and systems for a grid-based road model with multiple layers. An example road-perception system generates a roadway grid representation that includes multiple cells. The road-perception system uses data from multiple information sources to generate a road model that includes multiple layers. Each layer represents a roadway attribute of each cell in the grid and includes one or more layer hypotheses. In this way, the described techniques and systems can provide an accurate and reliable road model and quantify uncertainty therein.

Abstract: A vehicle includes a controller, programmed to responsive to identifying a first user via a mobile device, load a first non-person entity associated with the first user from the mobile device to configure vehicle settings, the first non-person entity including a driver setting, a first entertainment setting and a second entertainment setting; and responsive to identifying a second user entering the vehicle, switch from the first entertainment setting to a second entertainment setting.

Abstract: A system for navigating a host vehicle includes at least one electronic horizon processor to access a map representative of at least a road segment on which the host vehicle travels or is expected to travel, wherein the map includes one or more splines representative of road features associated with the road segment, localize the host vehicle relative to a drivable path for the host vehicle represented among the one or more splines, determine a set of points associated with the one or more splines based on the localization of the host vehicle relative to the drivable path for the host vehicle, and generate a navigation information packet including information associated with the one or more splines and the determined set of points relative to the one or more splines.

Abstract: An Anti-lock Braking System and control method are disclosed. The control method is performed after a control module intervenes a vehicle's braking system and comprises: receiving a wheel speed signal of a wheel and a vehicle acceleration signal; computing a tire-slip feedback value according to the wheel speed signal of the wheels and the vehicle acceleration signal; generating a feedback control voltage according to a tire-slip difference between a tire-slip target value and the tire-slip feedback value; generating a tire-slip compensation value by performing a differential compensation to the tire-slip feedback value; obtaining a feedforward voltage according to the tire-slip compensation value via a look-up table approach; generating a braking control voltage by adding the feedback control voltage to the feedforward voltage; and outputting the braking control voltage to a proportioning-valve brake, such that the proportioning-valve brake adjusts a braking pressure according to the braking control voltage.

Abstract: A travel control system for a vehicle includes a vehicle speed calculator, a mode continuation determiner, and a mode continuing unit. The vehicle speed calculator evaluates a level of worsening of a traveling environment and calculates a first vehicle speed on the basis of the level of the worsening of the traveling environment. The mode continuation determiner determines whether it is possible to continue with driving assist control in the second driving assist mode by comparing a second vehicle speed in the second driving assist mode with the first vehicle speed. When it is not possible to continue with the driving assist control in the second driving assist mode, the mode continuing unit lowers the second vehicle speed in the second driving assist mode to the first vehicle speed to allow the driving assist control in the second driving assist mode to continue.