A control system for a vehicle may include a forward-facing camera to capture a plurality of images of a road ahead of the vehicle and a processing device. The processing device may be configured to: provide feedback to a vehicle operator of the vehicle to change lanes to a new lane, in which the vehicle is not already traveling, based on the ending of a current lane, the ending of the current lane indicated by a first traffic cone identified in the plurality of images; and update a distance from the vehicle to a second traffic cone, based on a position of the vehicle, the second traffic cone used to constrain vehicle operation to the new lane.

A device for setting a target vehicle that sets a target vehicle to be subjected to driving assistance control of a host vehicle includes: a detection signal acquisition device capable of acquiring a first detection signal representing an object by an image, and a second detection signal representing the object by a reflection point; and setting control unit, which determines whether to set a forward object as a target vehicle, wherein if a movement history is not associated with the forward object, and a combination history is associated with the forward object, then as a selection threshold of a first determination parameter for determining whether to set the forward object as the target vehicle, a selection threshold is used such that the forward object is less likely to be selected as the target vehicle than with the selection threshold which would be used if a movement history is associated with the forward object.

A method and a system for predicting a near future path and an associated output control signal for a vehicle. Prediction sensor data, vehicle driving data, and road data are collected. An input control signal indicative of an intended driving action is received. The sensor data and the vehicle driving data are pre-processed to provide a set of object data comprising a time series of previous positions of a respective object relative the vehicle, a time series of the previous headings of the object, and a time series of previous velocities of the object. The object data, the road data, the vehicle driving data, the control signal, and the sensor data are processed in a deep neural network. Based on the processing in the deep neural network, a predicted path output and an output control signal are provided.

Systems, methods, devices, and techniques for generating object-heading estimations. In one example, methods include actions of receiving sensor data representing measurements of an object that was detected within a proximity of a vehicle; processing the sensor data with one or more preliminary heading estimation subsystems to respectively generate one or more preliminary heading estimations for the object; processing two or more inputs with a second heading estimation subsystem to generate a refined heading estimation for the object, the two or more inputs including the one or more preliminary heading estimations for the object; and providing the refined heading estimation for the object to an external processing system.

The present invention provides a travel control apparatus for controlling traveling of a self-vehicle to avoid another vehicle on a front side, the apparatus comprising: a calculation unit for calculating a relative speed between the self-vehicle and the other vehicle; a state detection unit for detecting an operation state of the other vehicle; and a control unit for controlling avoidance processing of avoiding the other vehicle and causing the self-vehicle to pass a lateral side of the other vehicle, wherein the control unit changes a separation distance to separate the self-vehicle and the other vehicle in the avoidance processing in accordance with the relative speed calculated by the calculation unit and the operation state detected by the state detection unit.

Systems and methods are provided for controlling an autonomous vehicle (AV). A map generator module processes sensor data to generate a world representation of a particular driving scenario (PDS). A scene understanding module (SUM) processes navigation route data, position information and a feature map to define an autonomous driving task (ADT), and decomposes the ADT into a sequence of sub-tasks. The SUM selects a particular combination of sensorimotor primitive modules (SPMs) to be enabled and executed for the PDS. Each one of the SPMs addresses a sub-task in the sequence. A primitive processor module executes the particular combination of the SPMs such that each generates a vehicle trajectory and speed (VTS) profile. A selected one of the VTS profiles is then processed to generate the control signals, which are then processed at a low-level controller to generate commands that control one or more of actuators of the AV.

Various technologies described herein pertain to generating an occupancy grid movie for utilization in motion planning for the autonomous vehicle. The occupancy grid movie can be generated for a given time and can include time-stepped occupancy grids for future times that are at predefined time intervals from the given time. The time-stepped occupancy grids include cells corresponding to regions in an environment surrounding the autonomous vehicle. Probabilities can be assigned to the cells specifying likelihoods that the regions corresponding to the cells are occupied at the future times. Moreover, cached query objects that respectively specify indices of cells of a grid occupied by a representation of an autonomous vehicle at corresponding orientations are described herein. An occupancy grid for the environment surrounding the autonomous vehicle can be queried to determine whether cells of the occupancy grid are occupied utilizing a cached query object from the cache query objects.

A control system is suitable for use in a motor vehicle and is configured and intended for using information concerning objects and/or driving-related information about another motor vehicle in order to distinguish real objects in the surroundings of the motor vehicle from erroneously detected objects, based on surroundings data that are obtained from at least one surroundings sensor situated on the motor vehicle and provided to the control system. Based on these surroundings data, an object in the surroundings of the motor vehicle is detected, and a distance and/or a relative speed and/or an angle between the motor vehicle and the object are/is determined. The object is then classified as an actually existing object or as an erroneously detected object, based on the determined distance and/or based on the determined relative speed and/or based on the determined angle.

A false target removal device and method for vehicles that can determine whether a sensor fusion target is a false target and remove the false target and a vehicle including the device are disclosed. The false target removal device may include a learning unit for receiving sensor fusion measurement information and learning one or more parameters based on the received sensor fusion measurement information, a falseness determination unit for, upon receiving current sensor fusion measurement information, determining whether the current sensor fusion measurement information is false based on the one or more parameters learned by the learning unit, and a sensor fusion target generation unit for removing false target information and generating a sensor fusion target based on the result of the determination by the falseness determination unit.

System, methods, and other embodiments described herein relate to transitioning a vehicle from an autonomous to a manual driving mode. One embodiment analyzes data from one or more vehicle sensors to detect, at a current vehicle position, features in a first detection region and a second detection region ahead of the vehicle; determines, for each of one or more hypothetical vehicle positions, which features detected at the current position, if any, lie within the first detection region at that hypothetical position; identifies, among the one or more hypothetical positions, at least one localization-failure position at which localization of the vehicle will fail due to insufficient features being detected within the first detection region at the at least one localization-failure position; and initiates a transition from the autonomous driving mode to the manual driving mode based, at least in part, on the at least one localization-failure position.