A system for controlling the jerk in an automatic/autonomous vehicle, said system comprising processing means, which comprise a jerk calculation module configured to acquire an electric acceleration signal and calculate a jerk value of said vehicle, and a plurality of operating modules, each configured to acquire an electric braking signal and said jerk value, one of said plurality of operating modules being selected by means of a selection signal, said selected module modifying the electric braking signal as a function of said jerk value and providing a modified electric braking signal to said braking system.

Ego actions for a mobile robot in the presence of at least one agent are autonomously planned. In a sampling phase, a goal for an agent is sampled from a set of available goals based on a probabilistic goal distribution, as determined using an observed trajectory of the agent. An agent trajectory is sampled, from a set of possible trajectories associated with the sampled goal, based on a probabilistic trajectory distribution, each trajectory of the set of possible trajectories reaching a location of the associated goal. In a simulation phase, an ego action is selected from a set of available ego actions and based on the selected ego action, the sampled agent trajectory, and a current state of the mobile robot, (i) behaviour of the mobile robot, and (ii) simultaneous behaviour of the agent are simulated, in order to assess the viability of the selected ego action.

The application disclose an autonomous-driving-based control method performed by a computer device. The method includes: acquiring scene information of a target vehicle; determining a current lane changing scene type of the target vehicle according to the scene information; recognizing, when the current lane changing scene type is a mandatory lane changing scene type, a first lane for completing a navigation travel route, and, when the first lane satisfies a lane changing safety check condition, controlling the target vehicle to perform lane changing operation according to the first lane. The second lane for optimizing the travel time is recognized according to the scene information when the current lane changing scene type is the free lane changing scene type. When the second lane satisfies the lane changing safety check condition, the target vehicle is controlled to perform lane changing operation according to the second lane.

[Problem] To provide an automatic driving robot control device and control method that enable a vehicle to be operated smoothly while also being caused to conform to a command vehicle speed with high accuracy.

[Solution] The present invention provides an automatic driving robot (drive robot) 4 control device 10 that controls the automatic driving robot 4, which is installed in a vehicle 2 and causes the vehicle 2 to run, such that the vehicle 2 runs in accordance with a defined command vehicle speed, wherein the automatic driving robot 4 control device 10 is provided with: a running state acquisition unit 22 that acquires a running state of the vehicle 2 including a vehicle speed and the command vehicle speed; an operation content inference unit 31 that infers, on the basis of the running state, an operation sequence, which is a sequence of operations of the vehicle 2 at a plurality of times in the future that causes the vehicle 2 to run in accordance with the command vehicle speed, by using an operation inference learning model 40 that was trained by machine learning to infer the operation sequence; and a vehicle operation control unit 23 that extracts, from each of the operation sequences inferred a plurality of times in the past, the operations corresponding to a control time for subsequently controlling the automatic driving robot 4, calculates a weighted sum of these extracted plurality of operations to calculate a final operation value, generates, on the basis of the final operation value, a control signal for controlling the automatic driving robot 4, and transmits the control signal to the automatic driving robot 4.

An information processing device according to the present disclosure includes: an acquisition unit that acquires a model having a structure of a neural network and input information input to the model; and a generation unit that generates basis information indicating a basis for an output of the model after the input information is input to the model based on state information indicating a state of the model after the input of the input information to the model.

An HMI control device, which controls an HMI device mounted on a vehicle. The HMI device presents information to a driver of the vehicle in a recognizable manner. The vehicle is capable of automated driving and travels following a preceding vehicle at a speed equal to or lower than a predetermined speed. The HMI control device is configured to: present attention information for calling attention of the driver when a congestion is determined to be cleared; acquire a behavior of the driver; and end a second task presented on the HMI device at an end timing corresponding to the behavior of the driver acquired after presentation of the attention information.

A driver assistance method and a driver assistance apparatus are provided, which may be applied to the field of autonomous driving or intelligent driving. The driver assistance method includes: determining that a driver is in an abnormal state; determining that a first operation performed by the driver on a first terminal is an abnormal operation; and performing first processing, where the first processing includes outputting indication information and/or control information, and the indication information or the control information indicates a second operation performed on the first terminal, or the first processing includes controlling the first terminal to perform the second operation.

System and techniques for test scenario verification, for a simulation of an autonomous vehicle safety action, are described. In an example, measuring performance of a test scenario used in testing an autonomous driving safety requirement includes: defining a test environment for a test scenario that tests compliance with a safety requirement including a minimum safe distance requirement; identifying test procedures to use in the test scenario that define actions for testing the minimum safe distance requirement; identifying test parameters to use with the identified test procedures, such as velocity, amount of braking, timing of braking, and rate of acceleration or deceleration; and creating the test scenario for use in an autonomous driving test simulator. Use of the test scenario includes applying the identified test procedures and the identified test parameters to identify a response of a test vehicle to the minimum safe distance requirement.

Provided are methods for efficient vehicle extent estimation, which can include bounding box generation. Some methods described include determining bounding boxes surrounding detected point clusters according to tangents to convex hulls of the point clusters, and minimizing continuous functions of distances between points and bounding box sides. Accordingly, best-fit bounding boxes are determined more efficiently and quickly, as well as more accurately. Systems and computer program products are also provided.

The present document relates to a simulation method for an autonomous vehicle, a method for controlling the autonomous vehicle, a device, an electronic apparatus, a computer-readable storage medium, and a computer program product. The method for the simulation of the autonomous vehicle comprises acquiring current state information of the autonomous vehicle; performing the simulation based on the current state information to acquire the prediction information of the autonomous vehicle; and sending the prediction information to the autonomous vehicle.