The disclosure relates to a lane change method and system, a storage medium, and a vehicle. The lane change method includes the following steps: receiving consecutive frames of condition information, the condition information including velocity information of a current vehicle, state information of an adjacent vehicle, and lane information; with the condition information as an input to a neural network, processing the condition information by means of the neural network, to obtain an initial lane change strategy; and correcting the initial lane change strategy based on a predetermined rule and the condition information, to generate and output a corrected lane change strategy. According to this lane change method, intelligent, safe and efficient lane change may be achieved during an autonomous driving or driving assistance process.

A vehicle control system includes a controller circuit in communication with a steering sensor and one or more perception sensors. The steering sensor is configured to detect a steering torque of a steering wheel of a host vehicle. The one or more perception sensors are configured to detect an environment proximate the host vehicle. The controller circuit is configured to determine when an operator of the host vehicle requests a take-over from fully automated control of the host vehicle based on the steering sensor. The controller circuit classifies the take-over request based on the steering sensor.

A driving support ECU initializes a target trajectory calculation parameter at a start of LCA; calculates, based on the target trajectory calculation parameter, a target trajectory function representing a target lateral position which is a target position of an own vehicle in a lane width direction in accordance with an elapsed time from the start of LCA; calculates a target control amount based on the target trajectory function; when a steering operation by a driver has been detected, again initializes the target trajectory calculation parameter; and recalculates the target trajectory function based on the target trajectory calculation parameter.

A lane detection system for a vehicle includes at least one sensor for sensing lane markers and objects in the vicinity of a host vehicle, a driver interface in the host vehicle, and a controller having control logic. The control logic receives signals from the at least one sensor, determines a present lane of travel based on the sensor signals received, determines that a time that the host vehicle has been in the present lane of travel meets or exceeds a predetermined time, and transmits an alert signal to the driver interface in response to the time meeting or exceeding the predetermined time.

An autonomous driving system configured to perform an autonomous driving of a vehicle includes a lane change determination unit configured to determine a necessity of an operation intervention for a lane change of the vehicle by a driver of the vehicle during the autonomous driving, a lane change request unit configured to request the driver to perform the operation intervention for the lane change of the vehicle when the lane change determination unit determines that it is necessary to perform the operation intervention for the lane change of the vehicle by the driver, and a mode switching unit configured to switch a steering torque mode for a lane keep control in the autonomous driving from a normal steering torque mode to a weak steering torque mode when the driver is requested to perform the operation intervention for the lane change of the vehicle.

When executing the autonomous lane change control of lane change of the subject vehicle from the subject vehicle lane in which the subject vehicle travels to adjacent lane, the autonomous lane change control is executed so as to accelerate the lateral speed of the subject vehicle in the subject vehicle lane and thereafter decelerate the lateral speed in the subject vehicle lane. As a result, it takes longer for the driver of the following vehicle to confirm the lateral movement of the preceding subject vehicle during the lane change of the subject vehicle from the start of the lane change than a lane change in which the lateral speed is decelerated in the adjacent lane. Therefore, it becomes easier to recognize the lane change.

A method increases fidelity of a lane localization function aboard a motor vehicle by receiving input signals indicative of a relative position of the vehicle with respect to a roadway. The input signals include GPS and geocoded mapping data, and an electronic turn signal indicative of activation of the turn signal lever. Sensor-specific lane probability distributions are calculated via the lane localization function using the input signals. The various distributions are fused via the localization function to generate a host lane assignment. The host lane assignment corresponds to a lane of the roadway having a highest probability among a set of possible lane assignments. An autonomous steering control action is performed aboard the motor vehicle using an Advanced Driver Assistance System (ADAS) in response to the host lane assignment. A motor vehicle has a controller that performs the method, e.g., by executing instructions from computer-readable media.

An autonomous vehicle detects a tailgating vehicle and uses various response mechanisms. A vehicle is identified as a tailgater based on whether its characteristics meet a variable threshold. When the autonomous vehicle is traveling at slower speeds, the threshold is defined in distance. When the autonomous vehicle is traveling at faster speeds, the threshold is defined in time. The autonomous vehicle responds to the tailgater by modifying its driving behavior. In one example, the autonomous vehicle adjusts a headway buffer (defined in time) from another vehicle in front of the autonomous vehicle. In this regard, if the tailgater is T seconds too close to the autonomous vehicle, the autonomous vehicle increases the headway buffer to the vehicle in front of it by some amount relative to T.

An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS uses various artificial intelligence (AI) based techniques (e.g., neural networks, reinforcement learning (RL) techniques, etc.) and models as part of its processing. For an inferring data point, for which a prediction is made by AVMS using an AI model, the AVMS checks how statistically similar (or dissimilar) the inferring data point is to the distribution of the training dataset. A score (confidence score) is generated indicative of how similar or dissimilar the inferring data point is to the training dataset. The AVMS uses this confidence score to decide how the prediction made by the AI model is to be used.

The disclosure relates to an automated lane changing method for a vehicle. The method includes: obtaining a plurality of areas in a target lane; calculating, based on information about an obstacle included in each area in the plurality of areas, an expected lane changing point for the area; and selecting, based on the expected lane changing point and an availability check result for the plurality of areas, a first area in the plurality of areas as a target area for final lane changing. The disclosure further relates to an automated lane changing device for a vehicle, a computer storage medium, and a vehicle.