A system and method for actuating a vehicle operation power mode that include receiving sensor data from at least one sensor of a vehicle. The system and method also include determining if at least one vehicle operation requirement is met based on analysis of the sensor data and actuating an electric powered operation mode of the vehicle based on determining that the at least one operation requirement is met. The system and method further include modifying an operation of an electric battery of the vehicle to power the vehicle through the electric battery from being charged by a fuel powered engine of the vehicle based on the actuation of the electric powered operation mode.

There is provided a control system for a vehicle comprising a powertrain comprising a plurality of energy sources and for transporting cargo, the control system being configured to optimise the control of the powertrain by accounting for variations in one or more properties of the cargo. More specifically a controller and related control system for the energy balancing of the vehicle taking into consideration such factors as fuel usage, power management between the various power generating and storage sub-systems, regenerative braking, terrain topology, weather and other environmental conditions, operation of vehicle peripherals and parasitic power demands in addition to cargo management and environmental needs and driver comfort and safety, as well as vehicle fleet management.

A vehicle control device is set to a second driving mode in which the task is not imposed on the driver and the first steering controller controls steering of the vehicle, sets the driving mode to a first driving mode in which a task of performing a predetermined amount of operations or more on a driving operator is imposed on the driver when the first steering controller is in a state in which the steering is not able to be controlled such that the vehicle travels in a lane in which the vehicle travels, and does not set the driving mode to a driving mode in which the first driving controller controls the vehicle, which is included in a plurality of driving modes and maintains a state in which a second steering controller is made to control the steering when the second steering controller controls the steering.

The present disclosure is directed to apparatus, methods, and non-transitory storage medium for controlling the maximum speed of a vehicle as that vehicle travels along a route. Apparatus and methods consistent with the present disclosure may receive location information from an electronic device that is located at a vehicle and may provide information to the electronic device at that controls the maximum speed of the vehicle as speed limits change along the route.

Methods, vehicles and systems for managing vehicle velocity along routes having traffic lights. The behavior of an individual vehicle is adjusted to account for surrounding vehicles to avoid inefficient outcomes across a group of vehicles. A vehicle is configured to determine the presence of other vehicles in a defined following distance, and uses the presence of such other vehicles to adjust its own travel strategy, with or without communication or coordination with, or control over, the other vehicles.

A system includes a processor and a memory. The memory stores instructions executable by the processor to determine an action for a vehicle based on an identified risk condition, to determine a minimum time for performing the action, and based on data including a vehicle battery state of charge, an outdoor temperature, and a vehicle mode of operation, to determine an available electrical power for performing the action. The instructions further include instructions to determine maximum permissible electrical energy discharge rate based on the minimum time for performing the action and the determined available electrical power, to determine a load control plan for a plurality of electrical devices in the vehicle based on the maximum permissible electrical energy discharge rate, an electrical load status of the vehicle, and load priority data; and to execute the determined load control plan.

A vehicle includes a powertrain, an inertial measurement unit configured to measure inertial forces exerted onto the vehicle, and a controller. The controller is programmed to control the torque at the powertrain based on a mapped relationship between the inertial forces and a vehicle velocity, wherein the mapped relationship utilizes at least one mapping parameter. The controller is further programmed to estimate a mass of the vehicle based on the mapping parameter.

Disclosed herein are an apparatus and method for adaptive autonomous driving control. The apparatus includes memory in which at least one program is recorded and a processor for executing the program. The program may perform control of a target vehicle by converting a theoretical control value based on a vehicle control algorithm into a hardware-dependent control value, which is dependent on the platform or hardware of the target vehicle, and may modify at least one parameter or a conversion equation for conversion of the hardware-dependent control value such that an error is minimized based on the difference between a response value according to the control of the target vehicle and a control value.

The present disclosure is directed to apparatus, methods, and non-transitory storage medium for controlling the maximum speed of a vehicle as that vehicle travels along a route. Apparatus and methods consistent with the present disclosure may receive location information from an electronic device that is located at a vehicle and may provide information to the electronic device at that controls the maximum speed of the vehicle as speed limits change along the route.

The present invention relates to determination of a state of a vehicle on a road portion. The vehicle includes an Automated Driving System (ADS) feature. At first, map data associated with the road portion, positioning data indicating a pose of the vehicle on the road, and sensor data of the vehicle are obtained. Then, a plurality of filters for the road portion are initialized. Further, one or more sensor data point(s) in the obtained sensor data is associated to a corresponding map-element of the obtained map data to determine one or more normalized similarity score(s). Now, based on the determined one or more normalized similarity score(s), one or more multivariate time-series data are also determined and provided as input to a trained machine-learning algorithm. Then, one of the initialized filters is selected by the machine learning algorithm to indicate a current state of the vehicle on the road portion.