An in-vehicle backup control apparatus includes a discharge circuit that discharges a power storage unit and a control unit that controls the discharge circuit. The control unit sets either one of a superimposable voltage or a supply completion voltage as an interruption threshold value, the superimposable voltage being set as a voltage condition of the power storage unit when electric power is supplied simultaneously to a plurality of target loads during an abnormal state, and the supply completion voltage being set in association with one of the plurality of target loads. The control unit interrupts or delays the supply of electric power to the one of the plurality of target loads to prohibit the plurality of target loads from simultaneously operating if a charge voltage of the power storage unit reaches a value less than or equal to the interruption threshold value during the abnormal state.

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.

Techniques are described for determining weight distribution of a vehicle. A method of performing autonomous driving operation includes determining a vehicle weight distribution that values for each axle of the vehicle that describe weight or pressure applied on a respective axle. The values of the vehicle weight distribution are determined by removing at least one value that is outside a range of pre-determined values from a set of sensor values. The method further includes determining a driving-related operation of the vehicle weight distribution. For example, the driving-related operation may include determining a braking amount for each axle and/or determining a maximum steering angle to operate the vehicle. The method further includes controlling one or more subsystems in the vehicle via an instruction related to the driving-related operation. For example, transmitting the instruction to the one or more subsystems causes the vehicle to perform the driving-related operation.

An environment detection device for a vehicle is includes a sensor unit for determining environment information of a surrounding area of the vehicle itself, with a temperature measurement unit, configured to measure at least one state temperature of the sensor unit, and with a controller, configured to trigger at least one action for reducing the state temperature if a prescribed temperature warning value is exceeded.

A system for a vehicle includes a plurality of sensors onboard the vehicle and a controller. A first sensor of the plurality of sensors is configured to detect lane markings on a roadway. The controller is configured to store data from the plurality of sensors. In response to receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings, respectively, based on the stored data; isolate one of the causes as a root cause for the misdetection of lane markings; and provide a response for mitigating the misdetection of lane markings on the roadway based on the root cause for the misdetection of lane markings.

A system for addressing failure in an autonomous agent includes a driving subsystem, a control subsystem, a central computing subsystem, and an autonomous vehicle (AV) sensor subsystem. The system can optionally additionally include a power subsystem, a vehicle chassis subsystem, a communication subsystem, a distributed computing and/or processing subsystem, a supplementary sensor subsystem, and/or any other components. A method for addressing failure can include any or all of: detecting and responding to a failure; and operating the vehicle. Additionally or alternatively, the method 200 can include any other processes.

System, methods, and computer-readable media for training an object path prediction model to reduce an uncertainty of a predicted path when the predicted path of an object adjacent to another object. The training penalizes an uncertainty area prediction associated with a predicted future location of a nearby object to an autonomous vehicle (AV) when the uncertainty area prediction overlaps with another object to which the first detected object would be adjacent at the predicted future location. The training also penalizes a set of predicted future locations that implies improbable vehicle kinematics, whereby the object path prediction model becomes trained to avoid predicting similar sets of predicted future locations with improbable vehicle kinematics.

Disclosed embodiments relate to opportunistically updating Electronic Control Unit (ECU) software in a vehicle. Operations may include receiving, at a controller in a vehicle, a wireless transmission indicating a need to update software running on at least one ECU in the vehicle; monitoring an operational status of the vehicle to determine whether the vehicle is in a first mode of operation in which an ECU software update is prohibited; delaying the ECU software update when the operational status is prohibited; continuing to monitor the operational status of the vehicle to determine whether the vehicle is in a second mode of operation in which the ECU software update is permitted; and enabling updating of the at least one ECU with the delayed ECU software update when it is determined that the vehicle is in the second mode of operations.

Systems and methods for an autonomous vehicle are provided. In one aspect, an autonomous vehicle includes a perception sensor and a processor configured to: receive detected roadway conditions data including roadway grade data from the perception sensor, retrieve mapped data having grade data, and determine that the roadway has a grade based on the detected roadway grade data and the retrieved roadway grade data. The processor can be further configured to, in response to determining that the roadway has a grade, determine that the grade of the roadway is greater than or equal to a predetermined high grade value and less than a predetermined grade limit, and in response to determining that the grade of the roadway is greater than or equal to the predetermined high grade value and less than the predetermined grade limit, operate the autonomous vehicle to change lane to a right-most lane.

A control allocation system for a vehicle includes an electric power steering (EPS) system, one or more redundant actuation systems for controlling a plurality of wheels of the vehicle, and one or more controllers in electronic communication with the EPS system and the one or more redundant actuation systems. The one or more controllers execute instructions to determine tracking errors and vehicle dynamics states based on a plurality of local path planning references and receive a fault signal indicating the EPS system is non-functional. In response to receiving the fault signal, the one or more controllers determine a plurality of corrective constraints in real-time. The one or more controllers solve a real-time constrained optimization problem for each sampling interval of the control allocation system to determine a plurality of control actions based on the plurality of corrective constraints and the tracking errors.