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.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

An object-tracking method includes: using the velocity of a track for tracking an object, generated from a point cloud related to the object, in a current recognition period (T) to obtain a current velocity-based heading angle of the track; accumulating scores associated with information about the current velocity-based heading angle and incorporating the accumulated scores into a heading history having regions sectioned for respective heading angles of the track; obtaining a history-based heading angle of the track using the heading history; and correcting the history-based heading angle using information about the shape of the track and determining the result of correction to be a final heading angle in the current recognition period (T).

A method and system for target detection of a vehicle is proposed. In the method and system, when the vehicle is turning, a warning signal according to a risk level of collision is generated at the right timing as a risk level of collision between the host vehicle and the other vehicle behind the host vehicle may be accurately identified by correcting a driving path of the host vehicle and position of the other vehicle on the basis of a driving state of the host vehicle.

Disclosed are a track mergence module and a method by which track deletion conditions are set to be varied depending on various situations and a track corresponding to the set track deletion conditions is deleted. The track mergence module includes a track deletion condition generation and storage unit, a phenomenon determination unit and a track mergence unit. A track to be deleted may be accurately deleted by generating the track deletion conditions so as to adaptively correspond to the performance of a sensor and various situations in which a host vehicle is placed, thereby being capable of erroneous braking of the autonomously driving host vehicle.

An object-tracking method includes using the velocity of a track for tracking an object, generated from a point cloud related to the object, in a current recognition period to obtain a current velocity-based heading angle of the track. The method includes accumulating scores associated with information about the current velocity-based heading angle and incorporating the accumulated scores into a heading history having regions sectioned for respective heading angles of the track. The method includes obtaining a history-based heading angle of the track using the heading history, correcting the history-based heading angle using information about the shape of the track, and determining the result of correction to be a final heading angle in the current recognition period.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

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 collision avoidance system for a subject vehicle. The system includes a secondary vehicle detection module that, based on at least one of inputs from sensors of the subject vehicle or a message transmitted by a secondary vehicle, detects the following: location, speed, and heading of the secondary vehicle, and whether the secondary vehicle intends to turn or change lanes. A warning module warns an operator of the subject vehicle of the secondary vehicle and the identified intention of the secondary vehicle, and modifies warning intensity based on the detected intention of the secondary vehicle to turn or change lanes. An adaptive cruise control module modifies at least one of speed and heading of the subject vehicle based on the detected intention of the secondary vehicle to turn or change lanes.