The present disclosure relates to a method comprising the following steps: receiving sensor data which have been generated by a sensor system, in a control module of a vehicle computer; inputting the sensor data into a safety algorithm to detect safety-relevant objects; inputting the sensor data into a comfort algorithm to detect comfort-relevant objects; estimating future states of the objects using an environment model which represents the environment of the vehicle and in which the objects are stored and tracked over time; calculating a safety trajectory taking into account safety rules and a comfort trajectory taking into account comfort rules based on the estimated future states of the detected objects; using the comfort trajectory to control the vehicle if the comfort trajectory satisfies the safety rules; and using the safety trajectory to control the vehicle if the comfort trajectory does not satisfy the safety rules.

A computer-implemented method and a system for training a computer-based autonomous driving model used for an autonomous driving operation by an autonomous vehicle are described. The method includes: creating time-dependent three-dimensional (3D) traffic environment data using at least one of real traffic element data and simulated traffic element data; creating simulated time-dependent 3D traffic environmental data by applying a time-dependent 3D generic adversarial network (GAN) model to the created time-dependent 3D traffic environment data; and training a computer-based autonomous driving model using the simulated time-dependent 3D traffic environmental data.

A driving assistance device executes processing relating to a behavior model of a vehicle. Detected information from the vehicle is input to a detected information inputter. An acquirer derives at least one of a travel difficulty level of a vehicle, a wakefulness level of a driver, and a driving proficiency level of the driver on the basis of the detected information that is input to the detected information inputter. A determiner determines whether or not to execute processing on the basis of at least one information item derived by the acquirer. If the determiner has made a determination to execute the processing, a processor executes the processing relating to the behavior model. It is assumed that the processor does not execute the processing relating to the behavior model if the determiner has made a determination to not execute the processing.

A control system controls a vehicle using a probabilistic motion planner and an adaptive predictive controller. The probabilistic motion planner produces a sequence of parametric probability distributions over a sequence of target states for the vehicle with parameters defining a first and higher order moments. The adaptive predictive controller optimizes a cost function over a prediction horizon to produce a sequence of control commands to one or multiple actuators of the vehicle. The cost function balances a cost of tracking of different state variables in the sequence of the target states defined by the first moments. The balancing is performed by weighting different state variables using one or multiple of the higher order moments of the probability distribution.

A dynamic adaptive cruise control method for a first vehicle includes a controller disposed within the first vehicle identifying a second vehicle in a direction of travel of the first vehicle. The controller determines a distance between the first vehicle and the second vehicle. The controller determines a safe travel distance between the first vehicle and the second vehicle based at least in part on a set of primary factors and a set of secondary factors. The controller modifies a cruise speed of the first vehicle to maintain at least the determined safe travel distance between the first vehicle and the second vehicle.

A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set detects a set of road users and, for each road user, a current state including a current speed and a current position. The data storage device includes a finite plurality of behavioral models. The data processor assigns a behavioral model to each road user, probabilistically estimates, for each road user, a belief state comprising a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position, according to the behavioral model assigned to each road user, and determines a risk of collision of the road vehicle with a road user, based on the probabilistically estimated future state of each road user. The output device outputs a driver warning signal or executes an avoidance action if the risk of collision exceeds a predetermined threshold.

Systems and methods for testing a hands-off detection algorithm. The method includes determining a plurality of system behavior test conditions for the algorithm and selecting an orthogonal array defining a plurality of test cases based on the plurality of system behavior test conditions. The method includes generating, for each of the test cases, an expected test outcome. The method includes for each of the test cases, conducting a test of with the vehicle based on the orthogonal array to generate a plurality of actual test outcomes and generating a response table based on the test outcomes, including a plurality of system behavior test condition interactions. The method includes determining, for each of the interactions, a result rating based on the expected test outcomes and the actual test outcomes and identifying, within the response table, which one or more of the test conditions exhibits a high failure condition.

Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

Techniques for using a set of variables to estimate a vehicle velocity of a vehicle are discussed herein. A system may determine an estimated velocity of the vehicle using a minimization based on an initial estimated velocity, steering angle data and wheel speed data. The system may then control an operation of the vehicle based at least in part on the estimated velocity.

Techniques for determining a warped occupancy grid fit to a vehicle trajectory are discussed herein. In some examples, a portion of memory may be allocated to an occupancy grid. Further, a warped occupancy grid can be warped and associated with an environment that an autonomous vehicle is traversing according to a trajectory and/or throughway. A transformation maybe be determined between the warped occupancy grid and the memory allocated to the occupancy grid. Sensor data can be received from a sensor associated with the autonomous vehicle and may be associated with the warped occupancy grid and stored in the occupancy grid. The autonomous vehicle may be controlled according to the warped occupancy grid by identifying sensor data returns in cells of the warped occupancy grid that may indicate a detection of an object in a path of travel of the vehicle.