A system that may include multiple driving related systems that are configured to perform driving related operations; a selection module; multiple fault collection and management units that are configured to monitor statuses of the multiple driving related systems and to report, to the selection module, at least one out of (a) an occurrence of at least one critical fault, (b) an absence of at least one critical fault, (c) an occurrence of at least one non-critical fault, and (d) an absence of at least one non-critical fault; and wherein the selection module is configured to respond to the report by performing at least one out of: (i) reset at least one entity out of the multiple fault collection and management units and the multiple driving related systems; and (ii) select data outputted from a driving related systems.

In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.

An autonomous driving apparatus and method for an ego vehicle that autonomously travels includes a first sensor to detect a vehicle nearby the ego vehicle, a memory to store map information, and a processor to control autonomous driving of the ego vehicle based on the nearby vehicle detected by the first sensor and the map information stored in the memory.

An abnormality detection device includes: an intake pressure sensor configured to detect an intake pressure of an internal combustion engine; an opening sensor configured to detect a throttle opening of the internal combustion engine; and a determination unit configured to determine, based on detection results of the intake pressure sensor and the opening sensor, whether the intake pressure sensor is abnormal. The determination unit determines, based on a result of intake pressure comparison between different throttle openings, whether the intake pressure sensor is abnormal.

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.

The present disclosure describes a method for monitoring operations of an automated driving system (ADS) of a vehicle. For each monitored operation the method includes: determining a geographical position of the vehicle; determining an intended path of the vehicle; and determining one or more intended parameters associated with performing a driving manoeuvre of said vehicle from the determined geographical position along the intended path. For each monitored operation the method further includes: obtaining one or more parameters associated with performing the driving manoeuvre of said vehicle from said determined geographical position; and retrieving, from a statistical model, data indicative of a statistical distribution related to one or more corresponding intended and/or obtained parameters for said intended path. Based on said retrieved data, determining whether there is an anomaly associated with said monitored operation; and taking at least one action of a set of predefined actions if an anomaly is determined.

The disclosure relates to a vehicle control system, a vehicle control method, a computer device, a computer-readable storage medium, and a vehicle. A vehicle control system according to an aspect of the disclosure includes: an autonomous driving control module configured to generate first planning information and second planning information in real time based on vehicle environment information, vehicle status information, and vehicle destination information, where the second planning information is used for safe parking; and a vehicle dynamic control module configured to receive the first planning information and the second planning information from the autonomous driving control module in real time, to generate a first control instruction based on the received first planning information or generate a second control instruction based on the received second planning information.

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 load driving device includes a synchronous rectifier circuit having a driving-side switching element and a reflux-side switching element; a driver control circuit controls the synchronous rectifier circuit; and a voltage monitor circuit that monitors whether the voltage of an output terminal of the synchronous rectifier circuit is within a predetermined voltage range; where the driver control circuit, upon receiving a diagnosis command, performs control so that when the driving-side switching element is switched from ON to OFF, the reflux-Side switching element is also switched to OFF; and the voltage monitor circuit detects a normal state when the voltage to be monitored is within a normal level during a period in which both the driving-side switching element and the reflux-side switching element are turned OFF.

System, methods, and other embodiments described herein relate to improving the prediction efficiency of autonomous/semi-autonomous vehicles. In one embodiment, the system generates a provisional prediction according to sensor data from at least one sensor of a subject vehicle. The prediction can be associated with an aspect relating to operating the subject vehicle along a path. The system analyzes the provisional prediction in relation to a subsequent prediction about the aspect to determine a correspondence between the provisional prediction and the subsequent prediction. In response to determining that the correspondence satisfies an inaccuracy threshold, the system can store the provisional prediction and the sensor data associated with the provisional prediction to log potential inaccuracies in generating predictions based, at least in part, on the sensor data.