The present disclosure relates to systems and methods for host vehicle navigation. Disclosed systems and methods may receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one pedestrian in the environment of the host vehicle; identify eyes of the at least one pedestrian represented in at least one of the plurality of images; and determine, based on analysis of the at least one of the plurality of images and based on the identification of the eyes of the at least one pedestrian in the at least one of the plurality of images, a looking direction of the at least one pedestrian.

Provided are methods for learning to identify safety-critical scenarios for autonomous vehicles. First state information representing a first state of a driving scenario is received. The information includes a state of a vehicle and a state of an agent in the vehicle's environment. The first state information is processed with a neural network to determine at least one action to be performed by the agent, including a perception degradation action causing misperception of the agent by a perception system of the vehicle. Second state information representing a second state of the driving scenario is received after performance of the at least one action. A reward for the action is determined. First and second distances between the vehicle and the agent are determined and compared to determine the reward for the at least one action. At least one weight of the neural network is adjusted based on the reward.

The subject matter described in this specification is generally directed to a system and techniques for controlling an autonomous vehicle. In one example, a first set of navigational inputs associated with a first time period is selected, where the first time period begins after a reference time. A second set of navigational inputs associated with a second time period is also selected, where the second time period begins after the first time period, and where the first time period and the second time period are different lengths of time. The autonomous vehicle is then navigated based at least in part on the first set of navigational inputs and the second set of navigational inputs.

Disclosed are methods and devices for predictive maintenance of at least one component of a road vehicle. The general principle is based on the combination of the ranges of values of parameters that represent the wear of a road vehicle component. The method includes observing the duration of use of combinations of ranges of values in order to deduce therefrom a wear profile which is compared with a predetermined wear profile. This mechanism makes it possible to reduce the amount of information to be stored.

A method is disclosed for operating a motor vehicle having a plurality of driver assistance systems, wherein the method includes setting, via an operator interface of the motor vehicle, an assistance degree parameter, which indicates the extent to which support for a driver by the driver assistance systems is desired. The method also includes setting, via the operator interface of the motor vehicle, at least one additional parameter, which is related to the driving operation of the motor vehicle. The method additionally includes specifying, by a processing device of the motor vehicle, at least one operating parameter for each driver assistance system, in accordance with which operating parameter the driver assistance system is operated, as a function of the assistance degree parameter and the additional parameter(s). A motor vehicle is also disclosed that performs the method.

A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.

A method for automated driving of a vehicle, comprising: provide function modules, wherein the function modules each comprise at least one subfunction for automated driving, and wherein the function modules are each assigned at least one module validity attribute that defines the context of the surroundings in which the particular function module is valid, provide at least one system configuration, wherein the at least one system configuration comprises at least one of the function modules, and wherein the at least one system configuration is assigned at least one configuration validity attribute that defines the context of the surroundings in which the at least one system configuration is valid, select one of the at least one system configurations, wherein the selection depends on a context of the surroundings and the at least one associated configuration validity attribute, control the vehicle, wherein controlling is based on the selected system configuration.

An apparatus includes a control section that performs: acquiring information related to a purpose of use of a passenger cabin part, the purpose of use being intended by a user who rides in the passenger cabin part; and setting a control parameter of a traveling part that carries the passenger cabin part, based on information related to a behavior of the user in the passenger cabin part, and on the information related to the purpose of use intended by the user.

In various embodiments, a driver sensing subsystem computes a characterization of a driver based on physiological attribute(s) of the driver that are measured as the driver operates a vehicle. Subsequently, a driver assessment application uses a confidence level model to estimate a confidence level of the driver based on the characterization of the driver. The driver assessment application then causes driver assistance application(s) to modify at least one functionality of the vehicle based on the confidence level. Advantageously, by enabling the driver assistance application(s) to take into account the confidence level of the driver, the driver assessment application can improve driving safety relative to conventional techniques for implementing driver assistance applications that disregard the confidence levels of drivers.

A system and method for using human driving patterns to detect and correct abnormal driving behaviors of autonomous vehicles are disclosed. A particular embodiment includes: generating data corresponding to a normal driving behavior safe zone; receiving a proposed vehicle control command; comparing the proposed vehicle control command with the normal driving behavior safe zone; and issuing a warning alert if the proposed vehicle control command is outside of the normal driving behavior safe zone. Another embodiment includes modifying the proposed vehicle control command to produce a modified and validated vehicle control command if the proposed vehicle control command is outside of the normal driving behavior safe zone.