A processor unit (3) is configured to execute an autonomous driving function of the motor vehicle (1) during a first instance such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function. The processor unit (3) is further configured to store a driver intervention, the driver intervention being performed by a driver of the motor vehicle (1) during the first instance while the motor vehicle (1) travels autonomously based on the execution of the autonomous driving function. Additionally, the processor unit (3) is configured to execute the autonomous driving function during a second instance, subsequent to the first instance, based at least in part on the stored driver intervention such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function according to the stored driver intervention.

In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

In accordance with an exemplary embodiment, a vehicle is provided that includes a body, a drive system, and a control system for controlling the adaptive cruise control functionality for the vehicle. The drive system is disposed within the body, and has adaptive cruise control functionality. The control system includes: one or more sensors disposed onboard the vehicle and configured to obtain sensor data for monitoring a driver of the vehicle while the vehicle is stopped during adaptive cruise control operation while a target vehicle in front of the vehicle has stopped; and a processor coupled to the one or more sensors and configured to provide instructions for automatically resuming movement of the vehicle, when the target vehicle resumes movement, based on the monitoring of the driver of the vehicle.

A vehicle driver assist system includes an expert evaluation system to fuse information acquired from various data sources. The data sources can correspond to conditions associated with the vehicle as a unit as well as external elements. The expert evaluation system monitors and evaluates the information from the data sources according to a set of rules by converting each data value into a metric value, determining a weight for each metric, assigning the determined weight to the metric, and generating a weighted metric corresponding to each data value. The expert evaluation system compares each weighted metric (or a linear combination of metrics) against one or more thresholds. The results from the comparison provide an estimation of a likelihood of one or more traffic features occurring.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

A method for controlling autonomous driving in a vehicle capable of the autonomous driving may include collecting vehicle travel status information and system status information during the autonomous driving, sensing a failure based on the system status information, identifying normally controllable actuators when sensing the failure, determining a risk degree corresponding to the sensed failure based on the normally controllable actuator information and the vehicle travel status information, determining a safety state based on normally controllable actuator information and the risk degree, and determining a failure safety strategy corresponding to the safety state.

An apparatus for controlling backward driving of a vehicle including: a driving trajectory generation unit configured to generate a driving trajectory for backward driving of an ego vehicle on a target path, using sensing information acquired while the ego vehicle drives forward along the target path; and a control unit configured to control the backward driving of the ego vehicle on the target path according to the driving trajectory generated by the driving trajectory generation unit, correct the driving trajectory using driving information of another vehicle, which has driven backward on the target path before the ego vehicle, when a change on the target path is sensed in comparison to during the forward driving of the ego vehicle during the process of controlling the backward driving of the ego vehicle, and control the backward driving of the ego vehicle according to the corrected driving trajectory.

A method evaluates a degree of fatigue of a vehicle occupant in a vehicle. A number of first fatigue indicators is provided which are determined according to computation rules from a plurality of first sensor values and each represent a degree of fatigue of the vehicle occupant. The first sensor values represent measured values of the vehicle and/or measured values relating to a current journey. A first metadata record is associated with each of the number of first fatigue indicators, wherein the first metadata records represent information about the characteristics of the sensors. The first sensor values are processed in the respective first fatigue indicators. A number of second fatigue indicators is provided which are determined according to computation rules from one or more second sensor values and each represent a degree of fatigue of the vehicle occupant. The second sensor values represent physiological and/or physical parameters of the vehicle occupants. A second metadata record is associated with each of the number of second fatigue indicators. The second metadata records represent information about the characteristics of the sensors. The second sensor values are processed in the respective second fatigue indicators. An overall fatigue indicator is determined which represents the degree of fatigue of the vehicle occupant by weighting the number of first fatigue indicators and the number of second fatigue indicators. The fatigue indicators are weighted according to the information about the characteristics of the sensors contained in the first metadata record and the second metadata record.

A vehicle travel control device executes vehicle travel control such that a vehicle follows a target trajectory. An automated driving control device generates a first target trajectory that is the target trajectory for automated driving of the vehicle. The vehicle travel control device further determines whether or not an activation condition of travel assist control is satisfied. When the activation condition is satisfied, the vehicle travel control device generates a second target trajectory that is the target trajectory for the travel assist control. When the second target trajectory is generated during the automated driving, the vehicle travel control device determines whether or not a cancellation condition is satisfied. When the cancellation condition is satisfied, the vehicle travel control device cancels both the first target trajectory and the second target trajectory, and decelerates the vehicle.

A vehicle with a hybrid drivetrain including a fuel-fed engine coupled to a first drive axle, an electric motor coupled to a second drive axle and an APU for providing electrical power at stopover locations, and further including a controller for determining a location of the vehicle, a location of a stopover location, determining a target SOC of a battery for operating the APU at the stopover location and operating a hybrid control system to provide the target SOC for the vehicle at the stopover location.