Techniques are described for providing a hands-off steering wheel detection warning. An example method can include a vehicle computer determining a real-time level of fatigue of a driver of an autonomous vehicle. The vehicle computer can determine an operating parameter associated with an environment in which the autonomous vehicle is traveling. The vehicle computer can generate, using a machine learning model, a predicted driving pattern of a second vehicle traveling in the environment. The vehicle computer can determine a time interval for providing a hands-off steering wheel detection warning based at least in part on the real-time level of fatigue of the driver, the operating parameter, and the predicted driving pattern of the second vehicle. The vehicle computer can identify a final time interval for providing a hands-off steering wheel detection warning. The vehicle computer can output the hands-off steering wheel detection warning after the final time interval has elapsed.

A method for managing a state of charge (SOC) of an energy store of a vehicle comprising storing an encoding of a dynamic weight value, computationally determining an estimated travel time to a stopover location, using the estimated travel time to modify the dynamic weight value to provide an updated dynamic weight value, and responsive to providing the updated dynamic weight value, increasing the SOC of the energy store while the vehicle travels over a roadway to provide a target SoC of the energy store when the vehicle arrives at the stopover location.

Embodiments herein relate to a method for handling a driver's use of an object displaying device comprised in a vehicle. The driver's use of the object displaying device is monitored. Based on the monitoring, a likelihood that the driver has detected a representation of the object when using the object displaying device is determined. The representation of the object is visible to the driver in the object displaying device, and the object is located in the surroundings of the vehicle.

A broadcast of a signal is received at a first system from a second system at a first time. From the signal, a location of a target associated with the second system and a velocity of the target are determined relative to a location of the first system and a velocity of the first system. At the first system, using the location and the velocity of the first system and using the location and the velocity of the target, a likelihood is computed of a collision between the first system and the second system. A notification is sent from the first system about the likelihood of collision responsive to the likelihood of collision exceeding a threshold likelihood.

Methods and systems are provided for detecting faults in a sensor and reconstructing an output signal without use of the faulty sensor. In one embodiment, a method includes: receiving, by a processor, sensor data indicating a measured value from a first sensor; receiving, by a processor, sensor data indicating measured values from a plurality of other sensors; computing, by a processor, virtual values based on a vehicle model and the sensor data from the plurality of other sensors; computing, by a processor, a residual difference between the measured value from the first sensor and the virtual values; detecting, by a processor, whether a fault exists in the first sensor based on the residual difference; and when a fault in the sensor is detected, generating, by a processor, a control value based on the virtual values instead of the measured value.

A system comprises a computer including a processor and a memory. The memory storing instructions executable by the processor to cause the processor to determine a perception zone of interest, via a trained perception model, based on at least one of a vehicle parameter or planning data, wherein the vehicle parameter comprises at least one of a vehicle direction or a vehicle speed and the planning data comprises a route and trajectory to be traversed by the vehicle; determine a vehicle route and trajectory based on a subset of sensor data, wherein the subset of sensor data corresponds to the perception zone of interest; and operate the vehicle to traverse the vehicle route and trajectory.

A vehicle is provided with an engine to provide drive torque and a braking system to provide brake torque. The vehicle is also provided with a controller that is programmed to limit vehicle speed to a target speed by controlling at least one of the engine and the braking system to modify its output torque, the target speed being dependent on brake pedal position and a clearance distance between the vehicle and an external object.

A device for operating a vehicle includes: a vehicle movement module for forming control signals for a control unit of an actuating system of the vehicle; an energy management module for managing energy which is available for a vehicle operation, the energy management module being configured to form further control signals for the control unit as a function of the available energy, and a prioritizer for prioritizing the control signals over the further control signals as a function of a vehicle position and/or a vehicle movement state for stabilizing the vehicle.

A control strategy for a hybrid electric vehicle powertrain having an engine, a motor, and a transmission includes operating the powertrain according to a motor power loss term that is adapted based on battery power supplied to the motor, motor power output, and an estimated motor power loss such that the motor power loss term changes over time and converges to a constant value to thereby be indicative of actual motor power loss.

A method for maneuvering a vehicle on a multi-lane road includes receiving a navigation command for carrying out a lane change maneuver from a second traffic lane of the road via a first traffic lane of a road to a deceleration lane of the road; determining a distance between the vehicle and a starting point of the deceleration lane, wherein the distance is determined using sensor data from an environment sensor and/or using digital map data; preparing the lane change maneuver from the second traffic lane to the first traffic lane depending on the distance; detecting a road sign indicating a distance to the exit; determining a piece of distance information indicated by the road sign; and determining the distance between the vehicle and the starting point of the deceleration lane in addition depending on the distance information.