Various embodiments of a method and apparatus for diagnosing the status of a vehicle using cognitive SMART sensors are disclosed. The SMART sensors have at least some processing power directly associated with the sensor. The SMART sensor that include an artificial intelligence (AI) core are is tightly coupled to the sensor within the SMART sensor. The sensor may be an audio sensor, such as a high quality microphone, that can record sounds that emanate from of the vehicle. Such sounds may be associated with one or more of the components of the vehicle. The AI core analyzes the sounds that emanate from the vehicle to determine the operational status of the vehicle. A communication module allows the result to be communicated to remotely located interested entities.

The present disclosure provides example autonomous driving apparatuses and computer program products. One example apparatus includes receiving vehicle attribute information and traveling information of a target vehicle from the target vehicle. Layer information of a first road section on which the target vehicle travels is obtained from an autonomous-driving-policy-layer based on the traveling information. A first autonomous driving policy for the target vehicle is obtained based on the layer information of the first road section and the vehicle attribute information of the target vehicle. The first driving policy is sent to the target vehicle.

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, the operating status and/or configuration of autonomous operation features of an autonomous or semi-autonomous vehicle may be determined, such as via an on-board computer system or mobile device, and/or then directly or indirectly wirelessly communicated via data transmission from the vehicle computer system or mobile device to a remote server. An adjustment to one or more risk levels associated with operation of the autonomous or semi-autonomous vehicle may also be determined, and an auto insurance policy, premium, or discount may be adjusted based upon the adjustment to the risk levels and presented to the customer for their review and approval. As a result, insurance cost savings may be passed onto risk averse customers that opt into to a rewards program.

A vehicle travel control device executes trajectory following control to make the vehicle follow a target trajectory. A delay time represents control delay of the trajectory following control. A delay compensation time is at least a part of the delay time. The trajectory following control includes: displacement estimation processing that estimates a displacement of the vehicle in the delay compensation time; and delay compensation processing that corrects a deviation between the vehicle and the target trajectory based on the estimated displacement to compensate the control delay. The displacement estimation processing is effective in an effective period and ineffective in an ineffective period. When the ineffective period is included in the delay time of the trajectory following control, the displacement estimation processing is executed in a temporary mode by using sensor-detected information in the effective period without using the sensor-detected information in the ineffective period.

Provided is an in-vehicle control system capable of reducing an increase in cost accompanying advancement of a fallback operation. Therefore, the in-vehicle control system includes: a plurality of control circuits 3 and 4 respectively including control units that perform data communication with each other; an external environment recognition sensor a5; and a plurality of wirings 11, 12, 13, and 14 connecting the external environment recognition sensor a5 and the plurality of control circuits 3 and 4. The plurality of control circuits 3 and 4 include: a first power supply unit that supplies power to the external environment recognition sensor a5 via a corresponding wiring among the plurality of wirings 11, 12, 13, and 14; and a first detection unit that detects an abnormality related to the power supplied to the external environment recognition sensor a5. A control unit in a control device including a first detection unit that has detected the abnormality is controlled to acquire data of the external environment recognition sensor a5 from a control unit included in the other control device.

Decalibration of a Lidar sensor of a vehicle is identified using a reference and verification measurement, each of which projects at least one line on a road surface. A position of the projected at least one lines from the reference and verification measurements are compared to determine whether the lines are displaced from each other by more than a specified amount, in which case degradation of the Lidar sensor is identified.

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.

Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

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.