Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

Systems and methods for providing continuous safe-driver training safely are provided. A safe-driving challenge may be presented to an operator of a vehicle. Data captured by sensors associated with the vehicle may be analyzed to determine whether the operator of the vehicle has completed the safe-driving challenge. Based on a determination that the operator of the vehicle has completed the safe-driving challenge, a notification may be provided to the operator (e.g., indicating to the operator that he or she has successfully completed the challenge). A processor may randomly select whether a reward is to be provided to the operator of the vehicle based on the determination that the operator of the vehicle has completed the safe-driving challenge. Moreover, if a reward is to be provided to the operator of the vehicle, a processor may randomly select a type of reward to be provided to the operator of the vehicle.

The disclosed embodiments include a onboard driver distraction determination system. The determination system includes a onboard sensing and computing system(s), which includes inertial sensor(s), internal sensor(s), and external sensor(s). The onboard system samples data from the sensor(s) during a driving session to determine steering activity metrics and driver behavior. A steering activity metric is a representation of the steering inputs by the driver during the driving session. Driver behavior is a representation of how distracted the driver is during the driving session. By performing the above mentioned steps, the system can provide an analysis of driver distraction and optionally, take control of the vehicle to avoid aberrant behavior.

System, methods, and other embodiments described herein relate to improving automated driving by testing for inputs during driving. In one embodiment, a method includes testing an input from a driver in a manual driving mode of a vehicle. The method also includes adapting a fixed time interval on a condition that a test result of the input satisfies criteria used to validate driver inputs. The method also includes monitoring, via an input system of the vehicle, for driver feedback according to the fixed time interval in an automated driving mode.

A vehicle includes a detection system configured to acquire data regarding operation of the vehicle, and a robotic driving device configured to provide robotic control of the vehicle. The vehicle also includes a control system configured to determine whether the robotic driving device is activated, such that the vehicle is in robotic driving mode; receive a request by a prospective operator of the vehicle to deactivate the robotic driving device to initiate a manual driving mode; determine whether the prospective operator is impaired based on the data; and selectively grant or refuse the request based on the determination.

An audio learning system, which is applied to a vehicle and provides a learning content to a user staying in the vehicle in audio manner, includes: a learning element storage unit storing multiple learning elements; an in-vehicle duration estimation unit estimating an in-vehicle duration during which the user is in the vehicle; a learning program generation unit generating one batch of a learning program to be completed within the in-vehicle duration estimated by the in-vehicle duration estimation unit by combining the plurality of learning elements; and an execution unit executing the learning program. When a driving load estimated by a driving load estimation unit is higher than a predetermined load, the learning program generation unit generates the learning program to mainly include the learning elements that have been executed.

A method for operating an electrically operated or also electrically operable motor vehicle provided with a rechargeable electric energy storage device associated with the drive motor of the motor vehicle. A target charging state is determined for the energy storage device and an operating strategy is determined for a route that is calculated, entered or predicted for the next trip, by which recuperative deceleration is enabled with a specifiable minimum amount for deceleration processes occurring along the route. A total mass of the motor vehicle, including optionally a trailer connected to the motor vehicle, deviating from an input normal value and an air resistance of the motor vehicle deviating from a predetermined normal value are taken into account.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A control system for an electric vehicle configured to simulate an engine stall which might occur in conventional vehicles while preventing the simulation of the engine stall in an unfavorable situation. A controller of the control system is configured to: execute an engine stall control to simulate a behavior of the conventional vehicle in a situation where an engine stall occurs by stopping a motor, when a virtual engine speed calculated by a virtual engine speed calculator falls below a predetermined speed; and execute a hold assist control to apply a brake torque to the wheel by the brake device upon execution of the engine stall control.