A driving assistance system for automated driving of a vehicle includes at least one processor unit, which is designed to perform the following when the driving assistance system is carrying out a maneuver of turning onto a street: to determine, on the basis of environment data of an environment sensor system of the vehicle, whether another, on-coming road user is blocking a lane of the street which corresponds to the vehicle; and when it is determined that the other road user is blocking the lane of the street which corresponds to the vehicle to carry out a situation evaluation in order to determine whether the vehicle should back up in order to allow the other road user to exit the street or whether the other road user should be requested to clear the lane by backing up.

A control device controlling the movement route of a transportation vehicle calculates a movement route to a target position of the transportation vehicle and, in a case where an obstacle exists on the calculated movement route, counts the number of detours indicating the magnitude of an influence of the obstacle on movement route calculation, and calculates the movement route again so as to avoid the obstacle.

Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: (i) acquiring a predicted object trajectory for an object, (ii) acquiring a set of anchor points along the lane for the SDC, (iii) for each one of the set of anchor points, determining a series of future moments in time when the SDC is potentially located at the respective one of the set of anchor points, thereby generating a matrix structure including future position-time pairs, (iv) for each future position-time pair in the matrix structure, using the predicted object trajectory for determining a distance between a closest object to the SDC as if the SDC is located at the respective future position-time pair, and (v) storing the distance between the closest object to the SDC in association with the respective future position-time pair in the matrix structure.

Provided are an autonomous driving control apparatus and method based on a Road-GNN. By using road graph-based data, a network can more accurately and efficiently understand road shape information, and driving performance is improved.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. When the second target trajectory is generated during the automated driving, the vehicle travel control device determines whether or not a cancellation condition is satisfied. When the cancellation condition is satisfied, the vehicle travel control device cancels both the first target trajectory and the second target trajectory, and decelerates the vehicle.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. When the second target trajectory is generated during the automated driving, or when the second target trajectory is generated during the automated driving and a priority condition for giving priority to the second target trajectory is satisfied, the vehicle travel control device executes the vehicle travel control by giving more weight to the second target trajectory than to the first target trajectory.

Example aspects of the present disclosure are directed to improved systems and methods for testing autonomous vehicle operation through the use of mobile test devices that are controlled at least in part in response to predictive motion planning associated with autonomous vehicles. More particularly, the motion of a mobile test device can be controlled based on the motion plan of an autonomous vehicle to cause desired interactions between the mobile test device and the autonomous vehicle. Motion planning data associated with the autonomous vehicle can be obtained by an autonomous vehicle (AV) test system prior to the autonomous vehicle implementing or completely implementing a motion plan. In this manner, the AV test system can proactively control the mobile test device based on predictive motion planning data to facilitate interactions between the mobile test device and the autonomous vehicle that may not otherwise be achievable.

Techniques to generate driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. Nodes of the path are assigned a time for action to avoid collision from the node. The generated scenarios may be simulated in a computer.

Described are navigational systems for vehicles including modular, field-swappable and field-configurable components and a plurality of operational modes.

Disclosed are systems and methods for dynamically re-routing a pick path to an alternate pick location in response to identifying a shorted product at a pick location. The dynamic decision to re-route an autonomous vehicle is based on completion scores representing a likelihood of completing order in an autonomous vehicle, a maximum remaining time, an additional pick time, and order priorities. Based on various weights for factors of the orders, a system may re-route the autonomous vehicle to fulfill a shorted order at an alternate location when no orders have a high likelihood of completion, the additional pick time associated with re-routing the autonomous vehicle to the alternate pick location is less than the maximum remaining time for each order on the autonomous vehicle, and/or none of the orders on the autonomous vehicle have a higher priority than the shorted order.