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.

An in-vehicle system includes a zone control unit and lower-level control units. The zone control unit includes: a power supply control unit configured to control power supply to each of the lower-level control units; a communication control unit configured to control communication with each of the lower-level control units; and an abnormality detection unit configured to detect presence or absence of an abnormality in each of the lower-level control units. In a case in which an abnormality for two or more of elements including a power supply current value, a communication response time, and a MAC address is detected in at least one of the lower-level control units, the abnormality detection unit is configured to recognize that the at least one of the lower-level control units is an unauthorized device.

A substitution apparatus for installation in a vehicle in which a plurality of in-vehicle control apparatuses are implemented, the substitution apparatus including a control unit and a substitute unit. The control unit is configured to control the substitute unit based on transmission data transmitted from the in-vehicle control apparatuses, specify an abnormal in-vehicle control apparatus based on the transmission data, disable the specified abnormal in-vehicle control apparatus, and apply, to the substitute unit, a program for exhibiting functions otherwise normally executed by the specified abnormal in-vehicle control apparatus. The substitute unit is configured to substitute for the disabled in-vehicle control apparatus by executing the applied program.

System, methods, and other embodiments described herein relate to a training system to train a driver about occurrences of anomalous driving events of automated vehicle systems. In one embodiment, a method includes determining, upon receiving a selection of a vehicle behavior from one or more anomalous driving events and a detected state change signal, whether the vehicle behavior affects one or more entities. The method includes assessing a state of the one or more entities to simulate the vehicle behavior according to a safety standard. The method includes triggering simulation of the vehicle behavior if the state satisfies a threshold. The method includes simulating the vehicle behavior by at least controlling the vehicle to simulate the vehicle behavior during automated driving of the vehicle.

Method for monitoring health status of a chassis system of a vehicle comprising a new electronic control unit, a set of sensors, and a communication bus configured to convey signal data, the new electronic control unit being configured to run a vehicle chassis system simulation module based on a vehicle main physics-based model of each subsystem of the chassis system of the vehicle, a first neural network module, a second neural network module, and a signal processing module, comprising the following steps implemented by the new electronic control unit to predict a first set of data-driven subsystem health statuses, provide a second set of physics-based subsystem health statuses, provide a set of consolidated subsystem health statuses, and depending on the set of consolidated subsystem health statuses, produce a support recommendation to correct failures indicated by the set of consolidated subsystem health statuses.

Disclosed embodiments relate to performing updates to Electronic Control Unit (ECU) software while an ECU of a vehicle is operating. Operations may include receiving, at the vehicle while the ECU of the vehicle is operating, a software update file for the ECU software; writing, while the ECU is operating, the software update file into a first memory location in a memory of the ECU while simultaneously executing a code segment of existing code in a second memory location in the memory of the ECU; and updating a plurality of memory addresses associated with the memory of the ECU based on the software update file and without interrupting the execution of the code segment currently being executed in the second memory location in the memory of the ECU.

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.

An automated guided vehicle (AGV) scheduling method is disclosed. All the possible paths of the current AGV are calculated using the A* search algorithm. Time windows information tables are calculated according to shortest paths. Time windows of executed tasks are compared to find optimal paths. Types of conflicts are determined and priorities of the AGVs are modified and the time window information tables are updated according to the comparison result. When an obstacle is detected or the current AGV is malfunctioning, abnormal issues are dynamically processed and the time window information tables of the tasks are modified as the current AGV reaches a target workstation.

An electronic control device includes a control command generation unit that generates and outputs a control command for controlling a first control object; a communication unit that performs communication with another electronic control device that controls a second control object; a communication abnormality determination unit that determines whether communication with the another electronic control device by the communication unit is abnormal; and a reset determination unit that determines whether the another electronic control device is reset based on a change in a sensor signal related to a state of the second control object when the communication abnormality determination unit determines that the communication with the another electronic control device is abnormal.

This document describes methods by which an autonomous vehicle deploys a simulation scenario while operating in a real-world environment. The vehicle's sensors collect perception data. During a run of the vehicle in a real-world environment, the vehicle's on-board computing system will: (i) receive the perception data; (ii) publish the perception data to a muxing tool of the on-board computing system; (iii) generate simulation data that identifies and labels one or more simulated objects in the environment; (iv) publish the simulation data to the muxing tool; (v) use the muxing tool to add at least a portion of the simulation data to the perception data to yield augmented perception data; and (v) use at least a portion of the augmented perception data to make one or more navigation decisions based for the autonomous vehicle.