A control device for a vehicle includes an upper limit value setting unit configured to set an upper limit value of an acceleration or deceleration of the vehicle, and a vehicle controller configured to control the vehicle such that the acceleration or deceleration does not exceed the upper limit value. The upper limit value setting unit is configured to change the upper limit value according to at least one predetermined condition.

An example operation includes one or more of receiving, by a server, data related to an environment associated with a target transport, analyzing, by the server, the data to determine if at least one adverse condition related to the environment exists, and responsive to existence of the at least one adverse condition, sending, by the server, a recommendation related to operation of the target transport in a safe mode to overcome the at least one adverse condition to the target transport.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

A method for determining information related to a lane change of a target vehicle includes obtaining information related to an environment of the target vehicle. The information related to the environment relates to a plurality of features of the environment of the target vehicle. The plurality of features are partitioned into two or more groups of features. The method further determines two or more weighting factors for the two or more groups of features. An attention mechanism is used for determining the two or more weighting factors. The method further determines the information related to the lane change of the target vehicle based on the information related to the environment of the target vehicle using a machine-learning network. A weighting of the plurality of features of the environment of the target vehicle within the machine-learning network is based on the two or more weighting factors for the two or more groups of features.

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.

In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

A system for controlling an intelligent network vehicle is provided, and the system comprises a sensor group configured to obtain sensor information; a sensing and positioning module configured to obtain sensing information and positioning information based on the sensor information; a planning and control module configured to determine vehicle planning control information based on the sensing information and the positioning information; a safety control module configured to determine safety control information based on the sensing information and the positioning information; a function assessment module configured to determine a vehicle state assessment result; a risk assessment module configured to determine a risk assessment result; a logical arbitration module configured to determine vehicle execution information by arbitrating the vehicle planning control information and the safety control information; and an execution module configured to control the vehicle driving based on the vehicle execution information.

A method for operating an automated motor vehicle during automated driving includes determining the current motor vehicle route data, a current trajectory of the motor vehicle, and environment information. The method also includes ascertaining a driving maneuver that is to be expected, associating this driving maneuver with one of the at least two motoring comfort levels, and outputting advice to the occupants of the motor vehicle if the current motoring comfort level changes as a result. The method further includes automatically and actively taking measures relating to the interior to adjust the interior to suit the motoring comfort level that is to be expected.

An object of the present invention is to provide a vehicle motion control device that generates a travel path capable of realizing comfortable ride comfort and high safety with a small physical quantity related to a vehicle behavior such as a longitudinal acceleration, a lateral acceleration, and a vertical acceleration generated when a vehicle passes or avoids with respect to a predetermined region such as unevenness on a course of a vehicle. Therefore, the vehicle motion control device includes a vehicle behavior prediction portion that predicts a physical quantity related to a vehicle behavior that occurs when a vehicle maintains a reference route toward a predetermined region on a course and a physical quantity related to a vehicle behavior that occurs when the vehicle shifts to an avoidance route for avoiding the predetermined region, and a path generation portion that generates a travel path defined by the reference route or the avoidance route in which the physical quantity is smaller than a defined value.

Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.