Provided is a mobile object control system mounted on a mobile object in which driver assistance control is executed based on information on a to-be-monitored object, comprising a first control system including a first processor configured to acquire information from at least one or more first sensors and to calculate first information on a to-be-monitored object from the acquired information, where the first control system can deliver a calculation result of the first information to a second control system, where the second control system includes a second processor configured to acquire information acquired from at least one or more second sensors different from the first sensors and to calculate second information on a to-be-monitored object based on the calculation result delivered from the first control system and information acquired from the second sensors.

An automatic drive device includes a fusion unit recognizing travel environment of a vehicle based on an output signal of a surrounding monitoring sensor and a control planning unit generating a control plan based on a recognition result of the fusion unit. A diagnostic device includes an abnormality detection unit that detects an abnormality of a field recognition system from the surrounding monitoring sensor to the fusion unit by monitoring the output signal of the surrounding monitoring sensor and the recognition result of the fusion unit over time. The diagnostic device requests the control planning unit to perform MRM based on a detection of the abnormality of the recognition system by the abnormality detection unit.

A vehicle guidance system includes a hardware processor. The hardware processor specifies, outside a vehicle, a route from a starting position to a target position in a parking lot, and transmits, to the vehicle over a communication network, information about at least one route section of the route. The hardware processor determines detection accuracy of a first monitoring device being set as an active monitoring device out of monitoring devices. The active monitoring device serves to monitor deviation of the vehicle from the route section during autonomous driving along the route section. In response to determining that the detection accuracy of the first monitoring device is lower than a reference accuracy, the hardware processor sets a second monitoring device as the active monitoring device in place of the first monitoring device. The second monitoring device is different from the first monitoring device among the monitoring devices.

Herein, a “perception statistical performance model” (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to receive a computed perception ground truth, and determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled. A simulated scenario is run based on a time series of such perception outputs (with modelled perception errors), but can also be re-run based on perception ground truths directly (without perception errors). This can, for example, be way to ascertain whether perception error was the cause of some unexpected decision within the planner, by determining whether such a decision is also taken in the simulated scenario when perception error is “switched off”.

A method for operating a driver assistance system of a vehicle is disclosed, wherein sensor data are recorded from the surroundings of the vehicle, the recorded sensor data are verified, the verified sensor data are analyzed by a neural network and analyzed sensor data are generated. Based on the analyzed sensor data, control data are generated for controlling the vehicle. During verification of the sensor data, at least first sensor data, which were recorded at a first, earlier point in time, are compared with second sensor data, which were recorded at a second, later point in time, the result of the comparison is cross-checked with a database in which data on perturbations to input data of a neural network are stored, wherein it is checked whether the second sensor data were generated at least in part by a perturbation to the first sensor data that is stored in the database.

The present disclosure provides a method and apparatus of monitoring a sensor of a driverless vehicle, a device and a storage medium, wherein the method comprises: monitoring a physical state of a to-be-monitored sensor; monitoring a data transmission state of the to-be-monitored sensor; monitoring output data of the to-be-monitored sensor, and using predetermined data to perform cross-validation for the output data; when any monitoring result gets abnormal, determining the to-be-monitored sensor as getting abnormal, and giving an alarm. The solution of the present disclosure may be applied to improve safety of the driverless vehicle.

In one example, a method for resolving sensor conflicts in autonomous vehicles includes monitoring conditions around the autonomous vehicle by analyzing data received from a plurality of sensors, detecting a conflict in the data received from two sensors of the plurality of sensors, sending a first instruction to an auxiliary sensor of the autonomous vehicle that is not one of the plurality of sensors, wherein the first instruction instructs the auxiliary sensor to gather additional data about the conditions around the autonomous vehicle, receiving the additional data from the auxiliary sensor, and making a decision regarding operation of the autonomous vehicle, wherein the decision is based at least in part on the additional data.

Systems and methods for monitoring a fleet of self-driving vehicles are disclosed. The system comprises one or more self-driving vehicles having at least one sensor for collecting current state information, a fleet-management system, and computer-readable media for storing reference data. The method comprises autonomously navigating a self-driving vehicle in an environment, collecting current state information using the vehicle's sensor, comparing the current state information with the reference data, identifying outlier data in the current state information, and generating an alert based on the outlier data. A notification based on the alert may be sent to one or more monitoring devices according to the type and severity of the outlier.

For monitoring the function of a driver assistance system, the environment of a vehicle is detected by at least one sensor, sensor data of the environment is generated, and at least one measurement variable is determined from the sensor data and is compared to reference values for the measurement variable to monitor the function of the driver assistance system. The result of the function monitoring is subsequently output and/or stored in accordance with the result of the comparison.

Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, an identity of a vehicle operator may be determined and a vehicle operator profile and/or operating data regarding autonomous operation features of the vehicle may be received after the vehicle operator opts into a rewards program and agrees to share their data. Autonomous operation and vehicle operator risk levels associated with operation of the autonomous or semi-autonomous vehicle may be determined. Based upon the risk levels and/or comparison thereof, one or more autonomous operation features may be disengaged. A preparedness level of the vehicle operator to assume or reassume control of operating the vehicle is determined prior to disengagement. If satisfactory, an alert is presented to the vehicle operator prior to disengagement of the autonomous operation features.