The present disclosure is directed generating candidate trajectories and selecting one of them to be implemented. In particular, a computing system can obtain an initial travel path for an autonomous vehicle. The computing system can obtain sensor data describing objects within an environment of the autonomous vehicle. The computing system can generate a plurality of trajectories for the autonomous vehicle based on the sensor data and the initial travel path. The computing system can determine whether the initial travel path, an offset profile, and a velocity profile meet flatness criteria. In response to determining that the initial travel path, the offset profile, and the velocity profile meet the flatness criteria, the computing system can combine the initial travel path, the offset profile, and the velocity profile into the respective trajectory. The computing system can determine a trajectory from the plurality of trajectories.

Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.

A method of determining a vehicle travel trajectory, an electronic device, a storage medium and a vehicle, which relate to a field of an artificial intelligence technology, in particular to a field of autonomous driving and intelligent transportation. A specific implementation solution includes: determining an initial path information for a vehicle; optimizing the initial path information to generate a target optimized path information; determining an optimized mapping relationship for velocity according to the target optimized path information and a first energy consumption constraint parameter; and determining an optimized trajectory for the vehicle according to the target optimized path information and the optimized mapping relationship for velocity.

Systems, methods, tangible non-transitory computer-readable media, and devices associated with vehicle control based on risk-based interactions are provided. For example, vehicle data and perception data can be accessed. The vehicle data can include the speed of an autonomous vehicle in an environment. The perception data can include location information and classification information associated with an object in the environment. A scenario exposure can be determined based on the vehicle data and perception data. Prediction data including predicted trajectories of the object can be accessed. Expected speed data can be determined based on hypothetical speeds and hypothetical distances between the vehicle and the object. A speed profile that satisfies a threshold criteria can be determining based on the scenario exposure, the prediction data, and the expected speed data, over a distance. A motion plan to control the autonomous vehicle can be generated based on the speed profile.

A vehicle control device includes power source, electrical storage device, electric motor connected to driving wheel, able to be driven by supply of electric power from electrical storage device and supplys regenerative electric power generated upon regenerative operation to electrical storage device, a road information acquisition part acquires road information related to scheduled traveling route of the vehicle, a control target segment extraction part extracts control target segment, in which change of remaining capacity of the electrical storage device equal to or greater than predetermined value is predicted, on scheduled traveling route, and a charge/discharge planning part plans charge/discharge of the electrical storage device based on vehicle traveling load in road from the vehicle to the control target segment, and the charge/discharge planning part determines the vehicle traveling load of the segment located before the control target segment according to attribute of the control target segment.

A method for controlling cruise driving of a vehicle performed by a controller is provided. The method includes determining whether a turning section is present on a road in front of the vehicle based on information of the road included in navigation information. When the turning section is present on the road, the turning section is divided into an entry area and an escape area. The entry area is an area where speed of the vehicle is adjusted from cruise driving speed to target deceleration speed and the escape area is an area where speed of the vehicle is controlled from the target deceleration speed to the cruise driving speed. When the vehicle is located in the entry area a starter-generator connected to an engine is operated to perform regenerative braking to reduce the speed of the vehicle from the cruise driving speed to the target deceleration speed.

A system and method for using human driving patterns to manage speed control for autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to desired human driving behaviors; training a human driving model module using a reinforcement learning process and the desired human driving behaviors; receiving a proposed vehicle speed control command; determining if the proposed vehicle speed control command conforms to the desired human driving behaviors by use of the human driving model module; and validating or modifying the proposed vehicle speed control command based on the determination.

A method for obtaining reference signals for vehicle control systems, in function of a vehicle geographical position along a travel route, includes providing data relating to the vehicle and data relating to a route to travel, and determining a vehicle driving force reference signal and a vehicle speed reference signal through a first optimisation process configured to optimise the driving force along the travel route. An engaged gear reference signal, in function of the positions of the vehicle along the travel route, is determined through a second optimisation process configured to optimise fuel consumption of the vehicle along the travel route. The second optimisation process is subsequent to the first optimisation process, and the data relating to the travel route, as well as the driving force reference signal and the speed reference signal, is received as input, determined through the first optimisation process.

The vehicle control device includes a start determination unit configured to determine whether or not the inter-vehicle distance between the preceding vehicle and the host vehicle has become equal to or greater than a start threshold greater in value than the stop threshold, and a vehicle control unit configured to start the host vehicle based on a preset speed profile after the start determination unit determines that the inter-vehicle distance between the preceding vehicle and the host vehicle is equal to or greater than the start threshold and maintain the stopped state of the host vehicle until the start determination unit determines that the inter-vehicle distance between the preceding vehicle and the host vehicle is equal to or greater than the start threshold after the preceding vehicle and the host vehicle stop.

A method for restricting the speed of a vehicle traveling in a curve. Receiving multiple road data samples including location points. Determining a parameter value for each of a first set of vehicle speed affecting parameters. Selecting a vehicle speed model based on the first set of parameter values. Determining a parameter value for each of a second set of vehicle speed affecting parameters. Calculating a recommended speed for the vehicle for at least one of the road samples based on the selected model and the parameter values of the second set of vehicle speed affecting parameters. Providing an instruction for adapting the vehicle speed to the recommended speed at the location point of the at least one road sample.