An autonomous driving system according to the present disclosure is an autonomous driving system that causes a vehicle to autonomously travel along a path plan, and includes a memory and a processor. The processor is configured to execute a determination process of determining whether offset processing is necessary, the offset processing offsetting the path plan in a lane width direction with respect to a reference traveling position in a changed lane after a lane change when the lane change is performed in response to that a preceding vehicle is present, and a process of generating the path plan related to the lane change so as to connect to the path plan that is offset in the changed lane after the lane change when the offset processing is necessary.

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that solves gridlock as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can determine one or more keep clear areas associated with the lane sequence, each keep clear area indicative of a region along the nominal path in which gridlock prevention is desired. A gridlock constraint can be generated for each of the one or more keep clear areas, each constraint being defined as a constraint area in a multi-dimensional space. A low-cost trajectory path can be determined through a portion of the multi-dimensional space that minimizes exposure to the constraint areas and that is consistent with all constraints generated for the one or more objects of interest and the one or more keep clear areas.

An autonomous vehicle uses probabilistic neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The probabilistic neural network is configured to receive an image of traffic as input and generate output representing hidden context for a traffic entity displayed in the image. The system executes the probabilistic neural network to generate output representing hidden context for traffic entities encountered while navigating through traffic. The system determines a measure of uncertainty for the output values. The autonomous vehicle uses the measure of uncertainty generated by the probabilistic neural network during navigation.

A platooning control apparatus may include a processor configured to detect the occurrence of acceleration and shifting of a vehicle in front of a host vehicle based on information received from the vehicle in front during platooning, and to set a feedforward control input value of a host vehicle to zero for controlling an inter-vehicle distance with the vehicle in front in a section in which the acceleration and the shifting of the vehicle in front occurs; and a storage configured to store data and algorithms driven by the processor.

Provided is an advanced driver assist system (ADAS) and a vehicle having the same. The ADAS includes: a communicator configured to communicate with a plurality of other vehicles; an obstacle detector configured to detect an obstacle in a surrounding and output obstacle information about the detected obstacle; and a controller configured to acquire distance information about a distance to a second vehicle travelling in the surrounding of the first vehicle among the obstacles based on the obstacle information detected by the obstacle detector during a cruise control mode, acquire travel information and position information of a third vehicle travelling in the surrounding of the second vehicle based on information received through the communicator, and controlling acceleration and deceleration based on the distance information with respect to the second vehicle, the travel information of the third vehicle, and the position information of the third vehicle.

Systems and methods for estimating values of dynamic attributes of autonomous vehicles are disclosed. A first vehicle includes an inertial measurement unit (IMU) configured to measure a dynamic attribute (e.g., rate of change of vehicle yaw angle) and correlate the measured attribute with one or more input variables (e.g., values of steering angle commands). The correlated data is used to generate a model that can be used in a second vehicle to predict a dynamic attribute based at least in part on variable values input from the second vehicle. As a result, it is not necessary for the second vehicle to have an IMU.

A non-transitory computer readable storage medium has instructions executed by a processor to obtain the relative speed between a first traffic object and a second traffic object. The separation distance between the first traffic object and the second traffic object is received. The relative speed and the separation distance are combined to form a quantitative measure of hazard encountered by the first traffic object. The obtain, receive and combine operations are repeated to form cumulative measures of hazard associated with the first traffic object. The cumulative measures of hazard are analyzed to derive a first traffic object safety score for the first traffic object.

Disclosed herein an apparatus for assisting driving of a vehicle includes a camera installed in the vehicle, the camera having a field of view around the vehicle and obtaining image data; and a controller configured to process the image data. The controller may identify at least one object located around the vehicle based on processing the image data, update a trajectory of the vehicle based on an interference between a trajectory of the at least one object and the trajectory of the vehicle, control at least one of a driving device, a braking device, and a steering device of the vehicle based on the updated trajectory of the vehicle.

An apparatus for assisting driving of a vehicle includes: a radar mounted on the vehicle to have a front field of view of the vehicle and configured to acquire detection data; and a controller including a processor, configured to process the detection data, and configured to identify an estimated collision time between the vehicle and a first preceding vehicle, located in front of the vehicle, based on processing of the detection data, and to control a braking system of the vehicle to brake the vehicle, in response to the estimated collision time being less than a reference time, wherein the controller is configured to increase a reference time for braking the vehicle, based on an acceleration and a travelling speed of the first preceding vehicle and an acceleration and a travelling speed of a second preceding vehicle, located in front of the first preceding vehicle.

Aspects concern a method for controlling a braking of a vehicle. The method including detecting a braking situation, determining a classification of the braking situation, selecting a braking profile based on the determined classification, and applying a deceleration based on the selected braking profile to maintain a safety distance based on the selected braking profile.