A method for monitoring operation of at least one electrical load for a vehicle. A comparison of an actual value of an operating parameter with four threshold values is performed. A monitoring signal is also generated dependent on a result of the comparison, the monitoring signal being generated with a first signal characteristic if the actual value lies between the first threshold value and the third threshold value or between the second threshold value and the fourth threshold value, or being generated with a second signal characteristic if the actual value lies above the third threshold value or below the fourth threshold value. The first signal characteristic effects activation of at least one safety function that maintains an active operating state of the at least one electrical load. The second signal characteristic effects deactivation of the at least one electrical load.

A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

A system for transportation includes a vehicle having a user interface, and a robotic process automation system wherein a set of data is captured for each user in a set of users as each user interacts with the user interface, and wherein an artificial intelligence system is trained using the set of data to interact with the vehicle to automatically undertake actions with the vehicle on behalf of the user.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

The present disclosure relates to a verification apparatus for a vehicle-mounted control apparatus having a first program processing unit that executes a current program, based on an output of a sensor and outputs a processing result to an actuator unit. Because the verification apparatus has a second program processing unit that executes the current program and outputs a processing result, a third program processing unit that shares the output of the sensor unit with the second program processing unit and that executes a new program and outputs a processing result, and a comparison determination unit that compares the respective outputs, it is made possible to perform a regression test effective for the new program at low cost, without affecting operation of the vehicle-mounted control apparatus.

A method for fault detection is adapted for a vehicle display fault detection system. The vehicle display fault detection system includes a vehicle system and a display system. The method for fault detection includes following steps: detecting whether a first state of the display system is abnormal or not; when the first state is abnormal, the display system is configured to generate a fault detection signal; classifying to generate a control signal according to the fault detection signal; and adjusting the first state of the display system to a second state.

Informed towing is provided. Towing information is identified, by a towing vehicle, with respect to a towed vehicle to be towed by the towing vehicle. A towed configuration of the towed vehicle is monitored. Responsive to the towed configuration of the towed vehicle being incorrect according to the towing information, a warning is displayed in the HMI indicating the incorrect towing configuration.

Systems and methods for a cloud-based server system configured for an On-Demand Autonomy (ODA) service are disclosed. The cloud-based server in communication with a first vehicle and a second vehicle to receive a trip request for the ODA service from the first vehicle, and seek an agreement to establish a virtual link with the first vehicle to the second vehicle by identifying one second vehicle by broadcasting the trip request to the group of second vehicles; identifying a second vehicle from submitted responses based on an application using a modeled clone with a set of functionalities to perform a matching operation between the first and second vehicles; and coordinating responses based on results of the matching operation to confirm an acceptance of the agreement; and creating a trip plan for the trip request for the ODA service based on a set of preferences received from each of the vehicles.

This disclosure is generally directed to systems and methods for providing a software sensor in a vehicle. In an example embodiment, a determination is made regarding the availability of a feature upgrade to a vehicle and a request may be made (to a cloud computer, for example), for obtaining the feature upgrade. The cloud computer provides an emulation software module based on emulating a first sensor that is unavailable in the vehicle. The feature upgrade may be installed in the vehicle by executing the emulation software module and by use of a second sensor that is available in the vehicle. In an example implementation, the second sensor available in the vehicle is a type of hardware sensor such as, for example, a camera, and the first sensor that is emulated is a different type of hardware sensor such as, for example, an air quality sensor.