In one embodiment, a control command is generated by an autonomous controller of the ADV. Feedback is sensed that corresponds to the control command. A difference is determined between a) the control command, and b) the feedback corresponding to the control command. If the difference is meets a threshold, then a fault response is generated.

The present disclosure provides a method and system for controlling a four-wheel-independent-drive (4WID) electric vehicle (EV) which incorporates the method steps of: acquiring driving environmental information of the vehicle, running state information of the vehicle and driving expectation information of a driver; tracking a body attitude; switching a condition of the vehicle according to information of an upper module; calculating an expected longitudinal torque, an expected lateral torque and an expected yaw torque of the vehicle that meet a driver's expectation; optimally distributing the torques of the vehicle; and generating armature voltage signals required by output torques of motors and controlling the motors. The method divides the driving process of the vehicle into multiple independent driving conditions. The method does not globally implement operation and control in multiple driving conditions with a single control strategy, but coordinately switches the conditions according to multiple control modes and multiple control strategies.

The travel assistance apparatus includes: a first Operating System (OS) that controls execution of at least one of a first application and/or a second application, the first application being for specifying a first travel control amount of a vehicle based on first movement information on a position and a speed of an object around the vehicle, the second application being for specifying a second travel control amount of the vehicle based on second movement information on a position and a speed of the object; a second OS that controls execution of a third application for performing travel control of the vehicle based on at least one of the first travel control amount and/or the second travel control amount; and a hypervisor that is executed on a processor and controls execution of the first OS and the second OS.

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.

Provided is a control apparatus for a vehicle configured to perform parking assist control, the control apparatus including a first power supply device, a second power supply device, and a power supply circuit, the power supply circuit being configured to, when an abnormality occurs in the first power supply device during the performance of the parking assist control, supply an electric power from the second power supply device to a braking device and a shift switching device, and the braking device and the shift switching device being configured to operate such that a timing at which a current flowing from the second power supply device to the braking device reaches a maximum value and a timing at which a current flowing from the second power supply device to the shift switching device reaches a maximum value do not overlap.

A vehicle having a control element for the speed, acceleration or direction of the vehicle, multiple identical or redundant computing devices (e.g., each implemented as a system on chip (SoC)) to separately generate driving commands in parallel during autonomous driving of the vehicle, and a command controller coupled between the control element and the computing devices. The commands may have one or more matching groups, where commands within each respective group agree with each other and thus vote for a candidate command representing the group. The computing device outputs a candidate command that represents the largest group for execution by the control element.

A computer-implemented method is provided for redundancy reduction for driving test scenarios. The method includes receiving an original test set of driving scenarios and a driving model which simulates a vehicle behavior under a driving scenario inputted to the driving model. The method includes, for each driving scenario of the original test set, obtaining vehicle dynamics timeseries data as an output of the driving model. The method includes determining similar driving scenarios by comparing driving model outputs. The method additionally includes creating a new test set of driving scenarios by discarding duplicated ones of the similar driving scenarios from the original test set.

A method includes identifying sensor data associated with corresponding distal ends of one or more axles of an autonomous vehicle (AV). The method further includes determining, based on the sensor data, mass distribution data of the AV. The mass distribution data is associated with a first load proximate a first distal end of a first axle of the AV and a second load proximate a second distal end of the first axle of the AV. The method further includes causing, based on the mass distribution data, performance of a corrective action associated with the AV.

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 method, apparatus, and system are described. The method includes generating a set of current values associated with at least one component included on a moving vehicle and providing the set of current values over a wireless network. The values are generated by one or more sensors. An edge computing device receives the current values. The method further includes processing the set of current values in real-time using at least one machine learning algorithm to identify a value of a point in time for a failure of one of the at least one component based on the set of current values and at least one set of past values received. The past values are stored in a memory. The set of current values are transmitted with a low time latency between the generating the set of current values and the processing of the set of current values.