In a self-driving autonomous vehicle, a controller architecture includes multiple processors within the same box. Each processor monitors the others and takes appropriate safe action when needed. Some processors may run dormant or low priority redundant functions that become active when another processor is detected to have failed. The processors are independently powered and independently execute redundant algorithms from sensor data processing to actuation commands using different hardware capabilities (GPUs, processing cores, different input signals, etc.). Intentional hardware and software diversity improves fault tolerance. The resulting fault-tolerant/fail-operational system meets ISO26262 ASIL-D specifications based on a single electronic controller unit platform that can be used for self-driving vehicles.

Disclosed embodiments relate to adjusting vehicle Electronic Control Unit (ECU) software versions. Operations may include receiving a prompt to adjust an ECU of a vehicle from executing a first version of ECU software to a second version of ECU software; configuring, in response to the prompt and based on a delta file corresponding to the second version of ECU software, the second version of ECU software on the ECU in the vehicle for execution; and configuring, in response to the prompt, the first version of ECU software on the ECU in the vehicle to become non-executable.

A method, an apparatus, a storage medium, and an electronic device for testing dynamic parameter of vehicle are provided. The method for testing dynamic parameter of vehicle provided by the present disclosure includes: first obtaining a control parameter for an autonomous vehicle; then controlling the vehicle to travel automatically under a given environment according to the control parameter, detecting and recording traveling data of the vehicle; and at last determining a dynamic parameter of the vehicle according to the traveling data. According to the method for testing dynamic parameter provided by the present disclosure, the characteristic of automatic driving of an autonomous vehicle is utilized to achieve an automatic measurement of the dynamic parameter, thereby reducing cost for calibrating the vehicle and significantly improving safety during the test. Additionally, human error caused by manually driving during the test can be avoided effectively.

A computer system for verifying vehicle software configuration may be provided. The computer system may include a processor and a non-transitory, tangible, computer-readable storage medium having instructions stored thereon that, in response to execution by the processor, cause the processor to: (1) transmit, to a vehicle computing system, an authentication request including a hash algorithm specification; (2) receive, from the vehicle computing system, a current configuration hash value and a vehicle identifier; (3) retrieve a trusted data block from a memory based upon the vehicle identifier, the trusted data block including a stored configuration hash value and a smart contract code segment; (4) execute the smart contract code segment, the smart contract code segment including a failsafe code segment; and/or (5) transmit the authentication response to the vehicle computing system, and cause the vehicle computing system to execute the failsafe code segment.

Disclosed embodiments relate to identifying Electronic Control Unit (ECU) anomalies in a vehicle. Operations may include monitoring, in the vehicle, data representing real-time processing activity of the ECU; accessing, in the vehicle, historical data relating to processing activity of the ECU, the historical data representing expected processing activity of the ECU; comparing, in the vehicle, the real-time processing activity data with the historical data, to identify at least one anomaly in the real-time processing activity of the ECU; and implementing a control action for the ECU when the at least one anomaly is identified.

The monitoring of an AI module of a vehicle driving function, the monitoring being performed using a generative-discriminative situation evaluation which represents a control mechanism for the AI modules along the processing chain of the driving function. The control mechanism is in the form of a monitoring unit for monitoring input and output data of a control module, in the form of an AI module, for semi-automatic or automatic driving.

This application relates to the field of automobile diagnosis technologies, and discloses a method and apparatus for remotely calibrating an advanced driver assistance system (ADAS), and a computer device. A diagnosis device may establish a remote connection to a client to directly remotely calibrate the ADAS of an automobile without the limitation of a distance. Therefore, a repair factory does not need to purchase diagnosis devices for all vehicle models. Instead, the diagnosis device can remotely calibrate the ADAS for the automobiles in the repair factory. In this way, the operation costs of the repair factory are greatly reduced. By means of the remote connection established between the client and the diagnosis device, the application of the diagnosis device is no longer limited to a specific area. Instead, the diagnosis device is applicable to any place where a remote connection can be established.

The in-vehicle control system in which a plurality of ECUs for controlling in-vehicle devices are connected to be able to communicate with each other is provided with an information set setting unit for setting an information set by combining information targeting different ECUs from among the original information relating to control functions of sound ECUs, an information collecting unit for collecting present information corresponding to the original information from each of the ECUs targeted by the information set, and an abnormality detection unit for detecting any one of the ECUs targeted to be abnormal when a degree of coincidence between correct answer information calculated by an evaluation function f with the information set as an argument and an evaluation value calculated by the evaluation function with the present information corresponding to the information set as an argument is lower than a criterion.

A position estimation system includes: a position estimation unit configured to estimate a position of a mobile terminal based on a reception status of signals that are transmitted from the mobile terminal and received by a plurality of communication devices; a scene determination unit configured to determine, based on information from a sensor mounted in a vehicle, whether a current status is a simultaneous operation scene in which both a millimeter wave radar and a radar vicinity device are operable simultaneously; and an operating mode changing unit configured to change an operating mode of at least one of the millimeter wave radar, the communication device, or the position estimation unit in response to the scene determination unit determining that the current status is the simultaneous operation scene.

A method may include identifying computing systems corresponding to an autonomous vehicle (AV) in which each computing system is configured to perform at least one operation relating to driving the AV. The method may include determining whether the computing systems use a first respective real-world parameter as an input to any of the operations or generate a second respective real-world parameter as an output of the operations. A respective operation corresponding to a respective computing system may be designated for accelerated performance based on the determination, and the operations of the computing systems may be performed. A first operation not designated for accelerated performance may include first computations corresponding to the first operation after waiting for a synchronization delay period, and a second operation designated for accelerated performance may include second computations corresponding to the second operation and performed without waiting for the synchronization delay period.