A method for monitoring a vehicle system configured to detect an environment of a vehicle, wherein the vehicle system has a sensor system with at least two sensor units configured to capture the environment of the vehicle, and an evaluation unit configured to detect objects in the environment of the vehicle by merging sensor data of the at least two sensor units. The method includes, for each of the detected objects, using sensor data to determine separately for each of the at least two sensor units a probability of existence indicating a probability of the detected object representing a real object, and a probability of detection indicating a probability with which the detected object can be captured by the sensors. The method includes determining whether the vehicle system is in a robust state based on the probability of existence and the probability of detection.

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 surrounding environment information generating unit acquires surrounding position information indicating a measured position of a side wall including a side wall of an evacuation spot, and generates measured map information for the side wall using the surrounding position information. When driving operation by a driver cannot be performed, a corrected map information generating unit calculates a correction value obtained by multiplying a difference between a distance from a traveling center line of a host vehicle to the side wall in base map information and that in the measured map information by a reliability, and generates corrected map information obtained by applying the correction value to the base map information. A route generating unit generates an evacuation travel route with the evacuation spot as a destination using the corrected map information. A vehicle control unit outputs a vehicle speed command for the host vehicle using the evacuation travel route.

A method for operating a vehicle, in particular, a vehicle for highly automated driving. The method includes a step of reading in input data. The input data include sensor data and sensor state data of a multitude of sensor units of vehicle. The method also includes a step of generating a potential field, using the input data. The input data are used as attractive potentials and repulsive potentials of the potential field. The method furthermore includes a step of determining a trajectory through the potential field in order to generate a fusion signal, using the trajectory, for fusing the input data for a sensor data fusion for a highly automated driving operation of the vehicle.

Techniques and methods for performing collision monitoring using system data. For instance, a vehicle may generate sensor data using one or more sensors. The vehicle may then analyze the sensor data using systems in order to determine parameters associated with the vehicle and parameters associated with another object. Additionally, the vehicle may determine uncertainties associated with the parameters and then process the parameters using the uncertainties. Based at least in part on the processing, the vehicle may determine a distribution of estimated locations associated with the vehicle and a distribution of estimated locations associated with the object. Using the distributions of estimated locations, the vehicle may determine the probability of collision between the vehicle and the object.

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 method for evaluating suitability route sections of a digital map storing landmarks for automated driving operation of a vehicle is provided. For each route section of the digital map a spatial density of landmarks is determined, an expected recognizability of the landmarks is determined by a vehicle sensor system under predetermined ambient conditions, a classification is performed based on the determined density and recognizability of the landmarks as to whether a vehicle can be located on the route section with a minimum accuracy required for a predetermined operating mode and/or for a predetermined driving maneuver, and a classification result is stored as a data record in a route attribute associated with the route section, the route attribute indicating for which of the predetermined operating modes and/or driving maneuvers requirements for the minimum accuracy of the landmark-based vehicle localization are met under which of the predetermined environmental conditions.

The invention relates to a method for operating a driver assistance device of a motor vehicle, comprising detection of an area surrounding the motor vehicle by a sensor device of the motor vehicle, which sensor device is associated with the driver assistance device, detection of at least one action by a driver of the motor vehicle, which action is related to a driving movement of the motor vehicle, by a further sensor device of the motor vehicle, which further sensor device is associated with the driver assistance device; learning of a correlation between the detected surrounding area and the detected action by the driver assistance device by means of repeated detection of the surrounding area and the action, evaluation of a degree of reliability of the learnt correlation by means of a quality measure by the driver assistance device, and implementation of at least one automated function, which is related to a driving movement of the motor vehicle, by the driver assistance device depending on the current area surrounding the motor vehicle and/or a value of the quality measure in order to provide a driver of the motor vehicle with surrounding area-dependent assistance by a driver assistance device as quickly as possible.

According to some embodiments, described herein is a method and a system for guaranteeing safety at a control level of an ADV when at least a portion of a planned path generated by a planning module of the ADV is uncertain due to traffics and/or road condition changes. The planning module, when generating a path, also generate a confidence level of each segment of the path based on one or more of perception data, map information, or traffic rules. The confidence levels are decreasing further away from the ADV. When the control module of the ADV obtains the path and the associated confidence levels, the control module issue control commands to track only one or two segments whose confidence levels exceeds a threshold hold, and issue default control commands for the rest of the path.

An obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.