An attention calling device includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: acquire information regarding obstacles around a moving object detected by a sensor included in the moving object; calculate a potential risk that is a degree to which attention needs to be paid for each of the obstacles around the moving object, based on the acquired information regarding the obstacles and a moving state of the moving object; and present, to an occupant of the moving object, information for calling attention to an obstacle having a potential risk exceeding a predetermined value, based on a calculated potential risk of each of the obstacles.

The technology relates to planning trajectories for self-driving vehicles in order to transport passengers or cargo from a pickup location to a destination. Trajectory planning includes generating a speed plan for an upcoming portion of the trip in view of one or more constraints. The constraints may be due to proximity to an adjacent vehicle or other road user, and can include projected overlaps between the vehicle and other objects in the vehicle's nearby environment. Certain constraints may be binary or otherwise discontinuous in nature, in which a condition either exists at a given point in time or it does not. Noise in sensor data or prediction models may trigger such binary conditions, which in turn may cause the vehicle to alter the speed plan. Aspects of the technology employ permeable speed constraints that enable the vehicle to avoid problems associated with discontinuous constraints.

A method for providing driving assistance by detecting and warning against areas on one or other side of the road which are obscured by vehicles in other lanes is based on a HD map and includes acquiring location and driving speed of a vehicle which is carrying an apparatus applying the method. The system of the method includes at least one sensor, and environmental information as to surroundings is acquired with location. The speeds of other vehicles relative to the driving speed of the vehicle are calculated, and an instruction to the driver is generated the speed of the vehicle is less than a first predefined value but the speed of the vehicle relative to the driving speeds of the other vehicles is larger than a second predefined value. The apparatus applying the method is also disclosed.

A method for simulation of an autonomous vehicle includes preparing a setting of a parameter and an initial value configured to determine a driving condition of the autonomous vehicle and a driving condition of an event to be performed by a surrounding vehicle to implement a simulation environment of the autonomous vehicle. The method further includes performing a normal driving in which the surrounding vehicle travels at a speed and a position that match a predetermined condition set in the parameter to perform the event given, and performing an event driving in which the surrounding vehicle performs the event given based on a setting value of the parameter.

A collision probability calculation device is provided with: a setting unit that sets a two-dimensional area composed of direction components of a first direction and a second direction, and including a first estimated position where a vehicle is estimated to arrive in the future from a reference position; a first variance calculation unit for calculating a first variance value of first position information in the two-dimensional area; a second variance calculation unit for calculating a second variance value of second position information on a second estimated position where an object is estimated to arrive in the future at the time when the vehicle arrives at the first estimated position; and a probability calculation unit for calculating the probability of a collision between the vehicle and the object, using the two-dimensional area, the first position information, the second position information, the first variance value and the second variance value.

A vehicle computing device may implement techniques to predict behavior of objects or predicted objects in an environment. The techniques may include using a model to determine whether a potential object will emerge from an occluded region in the environment. The model may be configured to use one or more algorithms, classifiers, and/or computational resources to predict an intersection point and/or an intersection time between the potential object and the vehicle. Based on the predicted intersection point and/or the predicted intersection time, the vehicle computing device may control operation of the vehicle.

This disclosure relates to method and system for validating an Autonomous Vehicle (AV) stack. The method may include receiving an Operational Design Domain (ODD) and real-world data for evaluating at least one of an Advanced Driver Assistance System (ADAS) and the AV. The ODD is based on at least one feature of at least one of the ADAS and the AV. For each of a plurality of iterations, the method may further include generating a driving scenario based on the ODD of the AV and the real-world data through a Quality of Ride Experience (QoRE)-aware cognitive engine, plugging and running at least one of the ADAS and the AV algorithm based on the driving scenario, and determining a set of performance metrics corresponding to the at least one feature of at least one of the ADAS and the AV in the driving scenario based on the simulating.

A method of path planning for a host vehicle includes: receiving host vehicle, environmental and obstacle information; calculating one or more projected host vehicle locations; computing a projected obstacle location for each obstacle; and determining a collision potential between each projected host vehicle location and each projected obstacle location. Until a maximum number of steps is reached, and while at least one projected host vehicle location has an associated collision potential below a collision threshold, the method further includes repeating the calculating, computing and determining steps.

Method of controlling a vehicle on a branch road, including determining whether a vehicle is traveling on a last lane of a road, monitoring movement in a lateral direction of a preceding vehicle, determining whether a curvature difference between a left lane and a right lane for the vehicle meets a threshold, determining whether an amount of movement in the lateral direction of the preceding vehicle is smaller than a reference movement, in response to the curvature difference between the left vehicle lane and the right vehicle lane meeting the threshold, and correcting vehicle lane information associated with an exit road side of the last lane using vehicle lane information associated with an opposite side of the last lane where the exit road is connected to the road, in response to the amount of movement in the lateral direction of the preceding vehicle being smaller than the reference movement.

Disclosed are an intersection driving control system and method for vehicles, the intersection driving control system for vehicles including a monitoring array configured to recognize and monitor peripheral vehicles driving proximate to a host vehicle, a pattern detector configured to detect reduction patterns of the peripheral vehicles in a longitudinal direction, and an intersection entry confirmer configured to confirm whether the host vehicle enters an intersection based on whether the longitudinal reduction patterns of the peripheral vehicles are similar to an intersection entry pattern.