A driver assistance system of a vehicle includes a forward viewing camera, a forward sensing non-vision sensor, and an ECU having at least one data processor. The ECU, responsive to processing of image data captured by the camera and to processing of sensor data captured by the non-vision sensor, provides a driving assistance function for the vehicle. The ECU detects presence of objects forward of the vehicle via processing of captured image data and captured sensor data. The ECU determines an error in object detection by determining difference between object detection based on processing of captured image data and object detection based on processing of captured sensor data. The ECU disables at least part of the driving assist function at least in part responsive to the determined error in object detection being greater than a threshold error level.

A method and an apparatus for obtaining information are disclosed. The method includes: determining that a first sensor in environment sensing sensors in a vehicle fails; determining a first detection area of the first sensor, where the first detection area includes a first angle range, and the first angle range is an angle range of a detection angle, of the first sensor, that covers a driving environment around the vehicle; adjusting a second detection area of a dynamic sensor in the vehicle, so that an angle range of the second detection area covers the first angle range, where the angle range of the second detection area is a range of a detection angle, of the dynamic sensor, that covers a driving environment around the vehicle; and obtaining environment information by using the dynamic sensor.

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.

The present teaching relates to method, system, medium, and implementations for sensor calibration. An ego vehicle determines whether a sensor deployed on the ego vehicle to facilitate autonomous driving of the ego vehicle needs to be calibrated and sends, if it is determined that the sensor needs to be calibrated, a request for assistance in collaborative calibration of the sensor, with a first position of the ego vehicle or a first configuration of the sensor with respect to the ego vehicle. When a response of the request is received, an assisting vehicle is indicated to travel to be near the ego vehicle to facilitate the calibration of the sensor by collaborating with the moving ego vehicle and the ego vehicle coordinates with the assisting vehicle to enable the sensor to acquire information of a target present on the assisting vehicle for the collaborative calibration of the sensor.

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.

Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

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.

According to an embodiment, provided are a method and an apparatus for monitoring an unmanned ground vehicle (UGV), for detecting a failure of a UGV actuator in consideration of terrain information. Accordingly, the accuracy of detecting a failure of the UGV is improved.

A driving assistance system includes a front camera module (FCM). The system, responsive to processing captured image data from the FCM, generates FCM control signals. The system includes a plurality of vehicle sensors capturing sensor data and an advanced driving-assistance system (ADAS) controller. The ADAS controller, responsive to processing the sensor data, generates ADAS control signals. The ADAS controller generates an ADAS status signal indicating a reliability of the generated ADAS control signals. With the ADAS status signal indicating the reliability of the generated ADAS control signals is at or above a threshold ADAS reliability level, the system controls the vehicle using the ADAS control signal and, responsive to determining that the ADAS status signal indicates that the reliability of the generated ADAS control signals is below the threshold, switches from controlling the vehicle based on the ADAS control signals to controlling the vehicle based on the FCM control signals.

The present application provides a point cloud data processing method, an apparatus, a device, a vehicle, and a storage medium, the method includes: acquiring, according to a preset frequency, raw data collected by sensors on a vehicle; and performing, according to the raw data of the sensors, data fusion processing to obtain a fusion result. By acquiring, according to the preset frequency, the raw data collected by the sensors on the vehicle in a latest period, and performing the data fusion processing to obtain the fusion result, a synchronous clock source may be removed, a weak clock synchronization may be realized, and the cost may be effectively reduced. The preset frequency may be flexibly set, which, when set with a larger value, can reduce a time difference between the raw data of the sensors and improve data accuracy.