A method includes: determining, by a computer device, conditions along a predefined driving route of a vehicle; determining, by the computer device and based on the conditions, risk factors of segments of the driving route; determining, by the computer device, an optimal one of the segments for switching the vehicle from autonomous driving mode to manual driving mode; and outputting, by the computer device, data defining the determined optimal one of the segments.

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: obtaining sensor data from sensors disposed to output data indicative of vehicle driver performance of a first vehicle, the first vehicle having for a first trip an associated first driver user who drives the first vehicle during the first trip; processing the sensor data to return error event flags that indicate errors of the first driver user when driving the first vehicle during the first trip; recording for respective event flags of the error event flags values of a first driving environment classification parameter and values of a second driving environment classification parameter; and predicting a driving performance of the first driver user for a second trip, the second trip being a proposed trip to occur after the first trip.

A hybrid vehicle including: an engine; a motor; a power storage device; and a control device configured to: i) automatically start and stop the engine; ii) execute, in a current trip, a power storage capacity decreasing control of controlling the engine and the motor such that a power storage capacity of the power storage device in a case where the hybrid vehicle is predicted to be parked at a predetermined point is lower than that in a case where the hybrid vehicle is predicted to not be parked at the predetermined point, and execute, in a next trip, a power storage capacity recovering control; and iii) limit execution of the power storage capacity decreasing control when the hybrid vehicle is predicted to be parked at the predetermined point is predicted and a stopping prohibiting condition for the engine is satisfied.

A system includes an acquisition unit that acquires an operation amount or a duration count, and a switching unit that switches a driving state. The switching unit switches the driving state to the cooperative driving state when the operation amount is equal to or greater than an intervention threshold and less than a start threshold or the duration count is equal to or greater than a first threshold and less than a second threshold during the autonomous driving state, switches the driving state to the autonomous driving state when the operation amount is less than the intervention threshold or the duration count is less than the first threshold during the cooperative driving state, and switches the driving state to the manual driving state when the operation amount is equal to or greater than the start threshold or the duration count is equal to or greater than the second threshold.

A travel control apparatus including a driving level switching portion switching to a first driving automation level involving a driver responsibility to monitor surroundings or a second driving automation level not involving the driver responsibility to monitor the surroundings, a distance measurement device measuring an inter-vehicle distance to a forward vehicle, and a microprocessor. The microprocessor performs controlling an equipment according to the inter-vehicle distance so as to follow the forward vehicle, controlling the equipment so that the self-driving vehicle starts when the inter-vehicle distance increases up to a predetermined value, and determining a first predetermined value as the predetermined value when the driving automation level is switched to the first driving automation level and a second predetermined value larger than the first predetermined value as the predetermined value when the driving automation level is switched to the second driving automation level.

A system for analyzing the environment of a vehicle i) receives a plurality of data from at least one sensor associated with a vehicle, such that the plurality of data includes at least one environmental condition at a location; (ii) analyzes the plurality of data to determine the at least one environmental condition at the location; (iii) determines a condition of a building at the location based upon the at least one environmental condition; (iv) determines an insurance product for the building based upon the determined condition associated with the building; and (v) generates an insurance quote for the insurance product. As a result, the speed and accuracy of insurance providers learning about potential clients and the conditions of the potential client's property and needs is increased.

A fuel economy and comfort control apparatus is provided for a motor vehicle. That apparatus includes a plurality of sensors and a controller. The controller is configured to determine and advise an operator of the motor vehicle of the most fuel efficient mode of operating a climate control system of the motor vehicle to maintain occupant comfort while optimizing fuel efficient operation of the motor vehicle. A related method is also disclosed.

System, methods, and other embodiments described herein relate to transitioning a vehicle from an autonomous to a manual driving mode. One embodiment analyzes data from one or more vehicle sensors to detect, at a current vehicle position, features in a first detection region and a second detection region ahead of the vehicle; determines, for each of one or more hypothetical vehicle positions, which features detected at the current position, if any, lie within the first detection region at that hypothetical position; identifies, among the one or more hypothetical positions, at least one localization-failure position at which localization of the vehicle will fail due to insufficient features being detected within the first detection region at the at least one localization-failure position; and initiates a transition from the autonomous driving mode to the manual driving mode based, at least in part, on the at least one localization-failure position.

The present disclosure relates to systems, devices and methods for vehicle control with natural-language guidance instructions. In one embodiment, a method is provided for control vehicle operation based on natural-language guidance instructions and image data detected by a camera mounted to a vehicle. One or more targets may be identified based on the guidance instructions to identifying vehicle position and/or operation. Object detection may be performed on the image data and control commands may be determined in response to the object detection. Operation of the vehicle may be controlled based on the control command. Natural-language guidance instructions can allow for directions and/or destinations to be provided without an operator or passenger of the vehicle having an address or map identifier of a destination. Processes and configurations are provided for confirming natural-language instructions and assessing image data to control vehicle operation.

Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a reference trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment. Portions of the reference trajectory can be identified as corresponding to actions to navigate around a double-parked vehicle or to change lanes, for example. In some cases, a portion of the reference trajectory can be identified based on a proximity to an object in the environment. A weight can be associated with the portions of the reference trajectory, and the techniques can include evaluating a reference cost function at points of the reference trajectory based on the associated weights to generate a target trajectory. Further, the techniques can include controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory.