A system for obtaining a prediction an action (a.sub.t) of a vehicle (V), including a camera for acquiring a sequence of images (F.sub.t) a convolutional neural network visual encoder which obtains a corresponding visual features vector (v.sub.t), one or more sensor that obtains a position of the vehicle (s.sub.t) at the same time step (s.sub.t), a Recurrent Neural Network configured to receive the visual features vector (v.sub.t) and position of the vehicle (s.sub.t) at the time step (t) and to generate a prediction of the action (a.sub.t) of the vehicle (V). The system comprising a command conditioned switch configured upon reception of a control command (c.sub.i) to select a corresponding branch of the Recurrent Neural Network. The system is configured to operate the selected corresponding branch to process the visual features vector (v.sub.t) and position of the vehicle (s.sub.t) at the time step (t) to obtain the prediction of the action (a.sub.t) of the vehicle (V).

The invention relates to a method for assisting a driver of a vehicle (1) having an electric drive, in which a list of predefined influencing variables for the consumption of electrical energy by the vehicle (1) is drawn up and output by an output device (14), with the influencing variables relating to factors which can be influenced by the driver of the vehicle (1), the method comprising the following steps: a) calling up characteristic maps which specify a relationship between energy consumption and the various influencing variables, b) determining possible optimizations of the energy consumption by modifying a particular influencing variable, c) computing possible energy savings on implementation of the possible optimizations of the particular influencing variable using the characteristic maps retrieved, d) sorting the influencing variables in the list.

A method for controlling autonomous driving of a vehicle according to the present invention comprises the steps of: constructing a safe stopping distance table including multiple preset stopping levels corresponding to multiple stopping distances determined based on multiple stopping variables required for calculating a safe stopping distance; acquiring current stopping variables required for calculating the safe stopping distance during autonomous driving; determining any one of the preset stopping levels as a current stopping level based on the acquired current stopping variables; selecting a certain stopping distance corresponding to the determined current stopping level as a current stopping distance, using the safe stopping distance table, and controlling a driving speed of the vehicle so that a distance between the vehicle and a preceding vehicle is maintained as the selected current stopping distance.

Provided are a vehicle driving control method and apparatus. The method includes: determining, according to driving data of a vehicle, at least one piece of candidate speed information of the vehicle, the candidate speed information is used to indicate traveling speed of the vehicle at each moment in a first preset time period; predicting, according to movement data of an obstacle on a road, an obstacle trajectory of the obstacle in the first preset time period; determining right-of-way information corresponding to the obstacle trajectory, the right-of-way information is used to indicate a priority in traveling of the vehicle and the obstacle; and determining target speed information in the at least one piece of candidate speed information according to the right-of-way information corresponding to the obstacle trajectory and the obstacle trajectory, and controlling the vehicle to travel according to a route corresponding to the vehicle and the target speed information.

A mobile work machine includes a propulsion subsystem that propel the mobile work machine about a worksite. The mobile work machine includes a steering subsystem that steers the mobile work machine about the worksite. The mobile work machine includes a stability determination system that determines a stability factor based on a characteristic of the steering subsystem. The mobile work machine also includes a control system that controls the mobile work machine based on the stability factor.

Systems and methods are provided for navigating a vehicle to veer around a lane obstruction safely into a neighboring lane. The system may plan a trajectory around the obstructed lane. Over a temporal horizon, the system determines temporal margins by measuring an amount of time between a predicted state of a moving actor in the neighboring lane and a predicted state of the vehicle. The system identifies a minimum temporal margin of the temporal margins and determines whether the minimum temporal margin is equal to or larger than a required temporal buffer. If the minimum temporal margin is equal to or larger than the required temporal buffer, the system generates a motion control signal to cause the vehicle to follow the trajectory to veer around the obstruction into the neighboring lane. Otherwise, the system generates a motion control signal to cause the vehicle to reduce speed or stop.

A computer includes a processor and a memory storing instructions executable by the processor to receive a waypoint; generate a path for a vehicle from a starting point to the waypoint, the path characterized by a preset number of parameters; and instruct a propulsion and a steering system of the vehicle to actuate to navigate the vehicle along the path.

The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

A method for controlling vehicle energy consumption includes identifying, using a cumulative net energy consumption of a vehicle traversing a route, one or more route segments of the route being traversed by the vehicle. The method also includes determining, for each route segment of the one or more route segments, a vehicle energy consumption profile and determining, for each of the one or more route segments, a profile for a target vehicle speed based on at least one route characteristic and a corresponding vehicle energy consumption profile. The method also includes, for a respective route segment of the one or more route segments generating one or more torque commands based on at least the target vehicle speed profile corresponding to the respective route segment, and selectively controlling, using the one or more torque commands, vehicle propulsion of the vehicle to achieve the target vehicle speed profile.

A method of manufacturing an autonomous cart is provided. The method includes determining a mission type for the autonomous cart and determining a power system for powering the autonomous cart based on the mission type. The method further includes determining a drive system suitable for converting a power delivered by the power system into motive power suitable for moving the autonomous cart based on the power system and the mission type. The method further includes installing the power system onto a chassis of the autonomous cart and installing the drive system onto the chassis of the autonomous cart, wherein the autonomous cart comprises a control system configured to drive the autonomous cart autonomously via the power system and the drive system.