Systems and methods are provided for implementing personalized adaptive cruise control techniques in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with one embodiment, a method comprises receiving first vehicle operating data and associated first environmental data of a plurality of vehicles; classifying the first vehicle operating data and the first environmental data into a plurality of driver type classifications; training a control policy model for each driver type classification based on the first vehicle operating data and the first environmental data; receiving a real-time classification of a target vehicle based on second vehicle operating data and associated second environmental data of the target vehicle; and output a trained control policy model the to target vehicle based on the real-time classification of the vehicle, wherein the target vehicle is controlled according to the trained control policy model.

A control apparatus and method for an autonomous vehicle includes a plurality of cores, and a scheduler that schedules a plurality of tasks corresponding to each operation mode of the autonomous vehicle for each of the cores, and adjusts an execution time of a task in the each core based on a current operation mode of the autonomous vehicle.

A vehicle for performing an autonomous parking function, the vehicle including: at least one camera configured to acquire a surrounding image of a vehicle; a display; a storage configured to store a HD map of a parking lot including an autonomous parking preparation zone; and at least one process configured to, based on the surrounding image of the vehicle being processed, generate a surround view image, based on an upper end of a road mark defining the autonomous parking preparation zone being recognized in the surround view image, track a position of the upper end of the road mark, upon the vehicle being stopped while a part of the road mark is recognized, correct a starting point on the HD map of the parking lot based on the position of the upper end of the road mark, and upon a user input for performing an autonomous parking function being received, perform the autonomous parking function based on the correct starting point.

An autonomous vehicle (an AV, or manual vehicle in an autonomous or semi-autonomous mode) includes the ability to sense a command from a source external to the vehicle and modify the behavior of the vehicle in accordance with the command. For example, the vehicle may visualize a police officer or other person associated with traffic control and interpret gestures made by the person causing the vehicle to stop, slow down, pull over, change lanes, back up or take a different route due to unplanned traffic patterns such as accidents, harsh weather, road closings or other situations. The system and method may also be used for non-emergency purposes, including external guidance for load pick-up/placement, hailing a vehicle used as a cab, and so forth. The command may further be spoken or may include a radio frequency (RF) light or other energy component.

An automated parking garage with one or more novel features described herein. These novel features include a modular design a control system that is operable to move multiple items, such as vehicles, independently so that they can move concurrently or simultaneously, at least one storage space that comprises an electric vehicle charging station, the ability to communicate with autonomous (e.g., self-driving) vehicles to facilitate parking of autonomous vehicles, a pallet cleaning system, and/or can be configured to store containers or other items.

A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with the host vehicle and identify a representation of a target object in the first output. The processing device may determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output and, if the at least one navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

Systems and methods are provided for vehicle navigation. In one implementation, at least one processor may be programmed to receive, from a camera, a captured image representative of features in an environment of the vehicle. The processor may generate a warped image based on the received captured image, which may simulate a view of the features in the environment of the vehicle from a simulated viewpoint elevated relative to an actual position of the camera. The processor may further identify a road feature represented in the warped image, which may be transformed in one or more respects relative to a representation of the road feature in the captured image. The processor may then determine a navigational action for the vehicle based on the identified feature represented in the warped image and cause at least one actuator system of the vehicle to implement the determined navigational action.

Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

A method for three-dimensional modeling. The method may include: acquiring coordinate points of obstacles in a surrounding environment of an autonomous driving vehicle in a vehicle coordinate system; determining a position of eyes of a passenger in the autonomous driving vehicle, and establishing an eye coordinate system using the position of the eyes as a coordinate origin; converting the coordinate points of the obstacles in the vehicle coordinate system to coordinate points in the eye coordinate system, and determining a visualization distance between the obstacles in the surrounding environment based on an observation angle of the eyes; and performing three-dimensional modeling of the surrounding environment, based on visualization distance between the coordinate points of the obstacles in the eye coordinate system and the obstacles.

A method for controlling a vehicle, an electronic device, a storage medium, and a vehicle are provided, related to a field of artificial intelligence technology, in particular to a field of autonomous driving and a field of computer vision. The method for controlling a vehicle includes: determining, in response to a request of switching to an autonomous driving mode, whether the vehicle is in a safe state; and controlling, in response to the vehicle being in the safe state, the vehicle to switch from a manual driving mode to the autonomous driving mode during travelling.