A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.

A navigational system for a host vehicle may comprise at least one processing device. The processing device may be programmed to receive a first output and a second output associated with the host vehicle and identify a representation of a target object in the first output. The processing device may determine whether a characteristic of the target object triggers a navigational constraint by verifying the identification of the target object based on the first output and, if the at least one navigational constraint is not verified based on the first output, then verifying the identification of the target object based on a combination of the first output and the second output. In response to the verification, the processing device may cause at least one navigational change to the host vehicle.

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.

According to a method for self-localization of a vehicle, a first pose of the vehicle is determined in a map coordinates system, based on environment sensor data representing an environment of the vehicle, a landmark is detected in the environment, a position of the landmark is determined in the map coordinates system and a second pose of the vehicle is determined in the map coordinates system dependent on the position of the landmark. An assignment instruction is consulted, matching up the first pose with at last one preferred sensor type or at least one dominant landmark type. Depending on the assignment instruction, a first environment sensor system is activated and a second environment sensor system is deactivated, whereupon the environment sensor data are generated by means of the first environment sensor system.

A vehicle includes a controller that identifies a target around the vehicle and calculates a danger range of the identified target, based on processing surrounding data obtained by sensor devices; calculates a danger range of the vehicle based on processing driving data obtained by sensor devices; determines a danger of collision based on the danger range of the target and the danger range of the vehicle, and control a driving apparatus based on the determined danger of collision. Such a vehicle and a control method thereof can make it possible to avoid a collision based on a danger range by calculating the danger range between the vehicle and a surrounding object of the vehicle depending on a factor causing uneasiness of a user.

A vehicular driver assistance system includes a front camera module (FCM) disposed at a vehicle. The system, responsive to processing captured image data, generates FCM lane information including information regarding a traffic lane the vehicle is currently traveling along. An e-Horizon module (EHM) generates EHM lane information including information regarding the traffic lane the vehicle is currently traveling along. The vehicular driver assistance system determines an FCM correlation using the FCM lane information and sensor data captured by at least one exterior sensor. The vehicular driver assistance system determines an EHM correlation using the EHM lane information and the sensor data captured by the at least one exterior sensor. Responsive to determining the FCM correlation and the EHM correlation, the system controls lateral movement of the vehicle based on one selected from the group consisting of (i) the FCM lane information and (ii) the EHM lane information.

Among other things, we describe techniques for implementing a vehicle response to sensor failure. In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include receiving information from a plurality of sensors coupled to a vehicle, determining that a level of confidence of the received information from at least one sensor of a first subset of sensors of the plurality of sensors is less than a first threshold, comparing a number of sensors in the first subset of sensors to a second threshold, and adjusting the driving capability of the vehicle to rely on information received from a second subset of sensors of the plurality of sensors, wherein the second subset of sensors excludes the at least one sensor of the first subset of sensors.

A method for operating an automated vehicle includes controlling by one or more computing devices an autonomous vehicle; receiving by one or more computing devices sensor data from the vehicle corresponding to moving objects in a vicinity of the vehicle; receiving by one or more computing devices road condition data; and determining by one or more computing devices undesirable locations related to the moving objects. The undesirable locations related to the moving objects for the vehicle are based at least in part on the road condition data. The step of controlling the vehicle includes avoiding the undesirable locations.

A driving assist device includes a first sensor, a second sensor, and a control device. The control device does not execute an inter-vehicle distance control under a predetermined first condition upon determination that at least one preceding object is detected based on the output of one of the first sensor and the second sensor without being detected based on the output of the other of the first and second sensors; and an environment of a non-detection sensor that is the other of the first and second sensors satisfies a first requirement for determination of a reliability of the output of the non-detection sensor; and the control device executes the inter-vehicle distance control under a predetermined second condition upon determination that the environment of the non-detection sensor satisfies a second requirement for determination of the reliability of the output of the non-detection sensor.

A method of initializing an automatic lane change in a moving primary automobile, including categorizing, via a controller within the primary automobile, a moving automobile that is in front of the primary automobile as a target automobile, categorizing, via the controller within the primary automobile, an object in front of both the primary automobile and the target automobile as one of a low confidence object and a high confidence object, initializing, via the controller within the primary automobile, the automatic lane change for the primary automobile when the object is categorized as a high confidence object, and initializing, via the controller within the primary automobile, the automatic lane change for the primary automobile when the object is categorized as a low confidence object and a lane change of the target automobile is detected by at least one of a plurality of sensors within the automobile.