Systems and methods are provided for navigating a host vehicle. In some embodiments, the system may include at least one processing device programmed to: receive at least one image representative of an environment of the host vehicle; determine a navigational action of the host vehicle; analyze the at least one image to identify a target vehicle in the environment of the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the navigational action was taken; determine a maximum braking capability of the host vehicle, a maximum acceleration capability of the host vehicle, and a speed of the host vehicle; determine a stopping distance for the host vehicle; determine a speed of the target vehicle; and implement the navigational action if the determined stopping distance for the host vehicle is less than the next-state distance summed with a target vehicle travel distance.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Intelligent driving control methods and apparatuses, vehicles, electronic devices, and storage media are provided. The method includes: obtaining a confidence degree of a detection result for at least one vehicle driving environment according to data collected by a sensor arranged in a vehicle; determining a driving safety level corresponding to the vehicle according to mapping relationships between confidence degrees and driving safety levels; and performing an intelligent driving control on the vehicle according to the determined driving safety level.

The present application discloses a method and an apparatus for autonomous driving control, an electronic device, and a storage medium; the application relates to the technical field of autonomous driving. A specific implementation solution is: obtaining movement data of a pedestrian, where the movement data includes a velocity component of the pedestrian along a width direction of a lane and a time of duration that the pedestrian cuts into a driving path of the autonomous vehicle from one side; determining a movement direction of the pedestrian according to the movement data and the movement information of the pedestrian; and generating a driving strategy for the autonomous vehicle according to the movement direction of the pedestrian. Therefore, the movement direction of the pedestrian can be accurately predicted, which facilitates the autonomous vehicle to avoid the pedestrian and insures driving safety.

A system can analyze a live sensor view of a self-driving vehicle (SDV) in accordance with a safety threshold to detect objects of interest along a route and classify each detected object of interest. When the safety threshold is not met, the system can transmit a teleassistance inquiry using LIDAR data to a backend computing system. When a certainty threshold is not met for an object of interest, the system can transmit a different teleassistance inquiry using image data to the backend computing system.

An image processing apparatus (10) includes a storage (13), a controller (14), and an output interface (15). The controller (14) detects an image of an object from a captured image. The controller (14) stores a newly detected state of the object by including the newly detected state in a state history of the object stored in the storage (13). The controller (14) determines an action of a moveable body (1) based on the state history stored in the storage. The output interface (15) outputs, to the moveable body, information with an instruction to perform the determined action of the moveable body (1).

A pedestrian protection apparatus may include: an active sensor configured to sense a forward obstacle of a vehicle; a passive sensor configured to sense a collision of the vehicle; a storage unit configured to store a collision threshold value which is set according to the passive sensor and a protection subject; a protection module driving unit configured to drive a protection module for protecting the protection subject in case of a collision with the vehicle; and a control unit configured to identify the protection subject based on the sensing result of the active sensor, adjust the collision threshold value according to the protection subject, compare the sensing result of the passive sensor to the collision threshold value, and operate the protection module driving unit.

A vehicular camera includes a lens barrel having a lens, a front camera housing having an aperture therethrough, and an imager printed circuit board (imager PCB) having an imager. The imager PCB is attached at the front camera housing with the imager aligned with the aperture. An attaching portion of the lens barrel is positioned at least partially in the aperture of the front camera housing with a gap between the attaching portion and the front camera housing that circumscribes the attaching portion. With the attaching portion positioned at least partially in the aperture, a filler material is disposed at least partially within the gap. After the lens is aligned relative to the imager, the filler material is heated to melt and flow into the gap, whereby the melted filler material hardens upon cooling to secure the lens barrel relative to the front camera housing and the imager PCB.

The present disclosure provides a task scheduling method, an apparatus, a device and a computer readable storage medium provided by the, and an implementation solution thereof includes: identifying obstacle information of an obstacle around a vehicle; determining a safety level of the obstacle according to driving information and the obstacle information of the vehicle; determining a driving task according to the obstacle information, determining a safety level of the driving task according to the safety level of the obstacle corresponding to the obstacle information; and performing a task scheduling according to the safety level of the driving task. The method, the apparatus, the device and the computer readable storage medium provided by the present disclosure can perform the task with the highest safety level preferentially and avoid a situation in which an urgent situation is unable to be dealt with in time.

A vehicle control apparatus is mounted in a vehicle and controls the vehicle. The vehicle control apparatus detects an obstacle that is present inside a grade crossing that intersects a traveling course of the vehicle, using a detection result of a sensor that is mounted in the vehicle. The vehicle control apparatus identifies a position and size of the obstacle using a detection result of the vehicle control apparatus, and calculates a clearance in a direction that intersects the traveling course when the vehicle crosses the grade crossing, using the identified position and size of the obstacle. The vehicle control apparatus determines whether the vehicle is able to pass through the grade crossing based on the calculated clearance.