Provided are a method for generating a lane changing decision-making model and a method and an apparatus for lane changing decision-making of an unmanned vehicle. The method for generating a lane changing decision-making model includes: obtaining a training sample set of vehicular lane changing, wherein the training sample set includes a plurality of training sample groups, each of the training sample groups includes a training sample under each time step length in a process that the vehicle completes lane changing based on a planned lane changing trajectory, the training sample includes a group of state variables and corresponding control variables; obtaining the lane changing decision-making model by training a decision-making model based on deep reinforcement learning network by use of the training sample set, wherein the lane changing decision-making model enables the state variable of the target vehicle and the corresponding control variable to be correlated.

Embodiments of the present application provide a collision detection method and apparatus based on an autonomous vehicle, a device and a storage medium, where the method includes: acquiring first point cloud data of each obstacle in each region around the autonomous vehicle, where the first point cloud data represents coordinate information of the obstacle and the first point cloud data is based on a world coordinate system; converting the first point cloud data of the each obstacle into second point cloud data based on a relative coordinate system, where an origin of the relative coordinate system is a point on the autonomous vehicle; determining, according to the second point cloud data of the each obstacle in all regions, a possibility of collision of the autonomous vehicle. A de-positioning manner for collision detection is provided, thereby improving the reliability and stability of collision detection.

A driving control method is provided in which a processor configured to control driving of a vehicle acquires detection information around a vehicle on the basis of a detection condition that can be set for each point; extracts events which the vehicle encounters, on the basis of the detection information; creates a driving plan in which a driving action is defined for each of the events on the basis of the detection information acquired in the events; executes a driving control instruction for the vehicle in accordance with the driving plan; and determines the detection condition on the basis of the content of the driving action defined for each of the events.

A lane departure prevention device includes a control unit that executes lane keeping control (automatic steering of a steering wheel and/or issuing of a warning) when it is determined that a vehicle may move out of a lane. The control unit withholds execution of the lane keeping control until it is determined that a return-to-control condition is satisfied when it is determined that a driver has gone from showing no intention to move out of the lane to showing an intention to move out of the lane to cross a first lane boundary. The control unit continues, when it is determined that the vehicle is approaching a second white line present in a traveling direction with a speed equal to or faster than a reference value, continues withholding the execution of the lane keeping control even when it is determined that the return-to-control condition is satisfied.

A side collision risk estimation system for a vehicle comprises a speed sensor, a road line markers detector, a movement sensor, an object detector, and a controller. The controller is configured to estimate: the current speed of the vehicle, a heading of the adjacent road line ahead of the vehicle, a heading of the vehicle, a compensated heading of the vehicle, a predicted lateral change position of the vehicle, a heading of a target vehicle relative to the vehicle, the current speed of the target vehicle, the current lateral distance between the vehicles, the heading of the adjacent road line ahead of the target vehicle, a compensated relative heading of the target vehicle, a predicted lateral change position of the target vehicle, a predicted lateral distance over time between the vehicles, and a side collision risk over time from the predicted lateral distance between the vehicles.

A vehicle control device includes a recognizer configured to recognize situations around a vehicle; a determiner configured to determine any of lanes included in a road on which the vehicle travels as a reference lane; and a driving controller configured to control at least one of a speed and steering of the vehicle according to the situations recognized by the recognizer and the reference lane determined by the determiner, wherein the determiner is configured to, when the vehicle moves from a first road to a second road different from the first road, among a plurality of lanes included in the first road, according to a relative position of a first reference lane determined at a time before the vehicle moves to the second road with respect to the plurality of lanes, determine a second reference lane on the second road.

An intersection start judgment device judges whether to start an own vehicle at an intersection at which vehicles in all directions need to temporarily stop. The device includes an autonomous sensor, a driving environment recognizer, a locator, and a controller. The autonomous sensor is disposed on the own vehicle and configured to detect a driving environment in front of the own vehicle. The driving environment recognizer is configured to recognize, with the autonomous sensor, the driving environment of the own vehicle. The locator is configured to calculate a location of the own vehicle with map information and a global navigation satellite system; and a controller configured to give, in a case where the own vehicle stopped temporarily at the intersection, start permission to the own vehicle when the number, counted by the driving environment recognizer, of other vehicles that are already stopping at the intersection becomes zero by subtraction counting.

A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.

An autonomous robot system to enable automated movement of goods and materials in a dynamic environment including one or more dynamic objects. The autonomous robot system includes an autonomous ground vehicle (AGV) including a vehicle management system. The vehicle management system provides real time resource planning and path optimization to enable the AGV to operate safely and efficiently alongside humans in a dynamic environment. The vehicle management system includes one or more processing devices to execute a moving object trajectory prediction module to predict a trajectory of a dynamic or moving object in a shared environment.

A method for trajectory planning, an apparatus, a storage medium, and an electronic device are provided. A constraint set of a space including a target device is determined according to a velocity of an unmanned device and velocities of designated obstacles, so that during optimization of a preliminary reference trajectory, a solution can be obtained with the space in the constraint set as a solution space under the constraint of the constraint set, so as to ensure that the solution space is a convex space, and relatively satisfactory reference trajectory points can be solved.