A method for generating an actuation signal for a driver assistance system and/or a system for the at least partially automated control of a vehicle. In the method, suggestions are made available for trajectories to be traveled by the vehicle and/or for other actions to be triggered that affect the driving dynamics of the vehicle. The suggestions are evaluated by a cost function, this cost function including a weighted sum of multiple cost terms, the weights being dynamically adapted. Utilizing the evaluations ascertained using the cost function, at least one trajectory or action is selected from among the suggestions. At least one actuation signal is generated that when conveyed to the driver assistance system or the system for the at least partially automatic control of the vehicle, induces the respective system to travel the selected trajectory with the vehicle or to trigger the suggested action.

Methods for extending a range of a vehicle are disclosed and include receiving a first data, the first data being indicative of a distance of the vehicle from a target destination, receiving a second data, the second data being indicative of a level of potential energy of an energy source for a power plant of the vehicle, receiving an operating parameter indicative of estimated future energy usage of the power plant, estimating, by a processor, an expected range of the vehicle based on the second data and the estimated future energy usage of the power plant, and adjusting a performance parameter of the power plant to extend an actual range of the vehicle when the estimated expected range is less than the distance of the vehicle from the target destination are disclosed. Systems for extending the range of the vehicle are also disclosed.

The system for range predication includes a pattern module, a consumption module, and a prediction module. The pattern module identifies a travel pattern of trips of a vehicle and receives vehicle data for the time period from the computing device of the vehicle. The travel pattern includes a path that is repeatedly traveled between an origin and a destination during a time period. The vehicle data includes historical range estimates for the vehicle along the path. The consumption module calculates energy consumption of the vehicle during the time period based on the vehicle data and determines actual remaining range values based on the energy consumption of the vehicle. The prediction module generates predictive range estimates along the path based on the actual remaining range and provides the predictive range estimates for a current trip.

A control apparatus of a plug-in hybrid electric vehicle that is provided with a fuel tank, an engine, a battery, and an electric motor. The battery is chargeable by an external power source. The control apparatus includes a fuel deterioration determiner and a regeneration amount limiter. The fuel deterioration determiner determines whether a fuel stored in the fuel tank is deteriorated. The regeneration amount limiter reduces, when the fuel is determined by the fuel deterioration determiner as being deteriorated, a regeneration amount to be less than the regeneration amount of a case where the fuel is determined by the fuel deterioration determiner as not being deteriorated. The regeneration amount is an amount of regeneration of electric power generated by the electric motor upon deceleration of the plug-in hybrid electric vehicle.

Disclosed herein is method and interactive assistance system for providing personalized assistance to a driver or person in an autonomous vehicle. Parameters related to the user and the vehicle are monitored and compared with historical data to determine a deviation in the parameters. An abnormal condition is detected when the deviation is more than an optimal threshold. Further, a personalized interaction is initiated with the user through a selected one of the interactive assistance engine and one or more assistive activities are performed for handling the abnormal condition. In an embodiment, the method of present disclosure enhances both safety and user experience of the user of the autonomous vehicle.

An autonomous driving control system of a vehicle includes a processor, a navigation, and a driving controller communicatively connected to one another. The processor is configured to estimate a charging amount of a power source that drives a driving device of a vehicle. The navigation is configured to set a driving route based on a destination and to search for a charging station for the power source based on the set driving route. The processor is further configured to determine a charging strategy of the power source based on the estimated charging amount of the power source, the driving route set by the navigation, and the searched charging station. The driving controller is configured to control driving of the vehicle based on the driving route set by the navigation and the determined charging strategy.

An example operation includes one or more of determining, by a transport, that an issue will soon occur, determining, by the transport, a time the issue will occur, and displaying, by the transport, the time the issue will occur. The issue is based on sensor data approaching a threshold within a period of time that is faster than an average period of time.

A system includes an inertial navigation system module (INS module) that detects vehicle yaw rates and vehicle lateral speeds, a controller circuit communicatively coupled with the INS module. The controller circuit determines a tire cornering stiffness (C.sub.f, C.sub.r) based on vehicle physical parameters and vehicle dynamic parameters. The controller circuit determines a vehicle moment of inertia (Iz) based on the vehicle physical parameters, the vehicle dynamic parameters, and the tire cornering stiffness (C.sub.f, C.sub.r).

Aspects of the present disclosure relate to a system having a memory, a plurality of self-driving systems for controlling a vehicle, and one or more processors. The processors are configured to receive at least one fallback task in association with a request for a primary task and at least one trigger of each fallback task. Each trigger is a set of conditions that, when satisfied, indicate when a vehicle requires attention for proper operation. The processors are also configured to send instructions to the self-driving systems to execute the primary task and receive status updates from the self-driving systems. The processors are configured to determine that a set of conditions of a trigger is satisfied based on the status updates and send further instructions based on the associated fallback task to the self-driving systems.

Method and system for providing carbon offsets. For example, the method includes collecting mindful driving data for one or more vehicle trips made by a user, analyzing the mindful driving data to determine a level of mindful driving of the user, determining a level of carbon offset reward based at least in part upon the level of mindful driving of the user, determining an amount of total carbon emission of the user, and providing an amount of carbon offset reward based at least in part upon the level of carbon offset reward and the amount of total carbon emission.