Hitachi Astemo Ltd.

Hitachi, Ltd.

Akshay Gokhale

Atsushi Yokoyama

Subrata Kumar Kundu

Yashodeep Dilip Lonari

Hiroaki Itsuji

In some examples, a system monitors for a memory fault associated with execution of software. Based at least on receiving an indication of a possible memory fault, the system scans a first area of the memory based on a data structure that indicates that the first area of the memory stores a portion of parameters of the software that affect an accuracy of the software if there is a memory fault in the first area of the memory. Based at least on the scanning indicating that there is a memory fault in the first area of the memory, the system remaps the portion of parameters of the software from the first area to a second area of the memory that is determined to be free.

Memory fault management for software

In some examples, a system receives sensor data from sensors on board a vehicle. Based at least on the sensor data indicating an anomaly has occurred and is affecting a travel path of the vehicle, an uncertainty threshold is compared with an uncertainty value associated with a prediction model. For instance, the uncertainty value may be indicative of a difference between a predicted value predicted by the prediction model and an actual measured value related to at least one operation parameter of the vehicle. Based at least on determining that the anomaly has occurred and that the uncertainty value is within the uncertainty threshold, sending, by the system, a communication for obtaining an updated prediction model.

Verification of vehicle prediction function

In some examples, a system may receive location information. The system may determine at least one landmark based on the location information. In addition, the system may determine one or more current conditions for the at least one landmark. Further, the system may receive sensor configuration information. Based on the sensor configuration information and the one or more current conditions, the system may determine the detectability of the at least one landmark.

Determining landmark detectability

In some examples, a vehicle suspension for supporting, at least in part, a sprung mass, includes a damper connected to the sprung mass, the damper including a movable piston. The vehicle suspension further includes an actuator and a controller. The controller may be configured to determine a frequency of motion associated with the sprung mass. When the frequency of motion is below a first frequency threshold, the controller may send a control signal to cause the actuator to apply a deceleration force to the sprung mass. Further, when the frequency of motion associated with the sprung mass exceeds the first frequency threshold, the controller may send a control signal to cause the actuator to apply a compensatory force to the sprung mass. For instance, a magnitude of the compensatory force may be based on a friction force determined for the damper.

Controlling damper friction effects in a suspension

To localize a vehicle in the given map precisely for autonomous driving, the vehicle needs to compare the sensor perceived environment with the map information accurately. To achieve this, the vehicle may use information received from multiple on-board sensors in combination of GPS and map data. Example implementations described herein are directed to maintaining accuracy of sensor measurements for autonomous vehicles even during sensor failure through a raw data fusion technique and a repopulation of data through a prediction technique based on historical information and statistical method if any of the sensors fail.

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Farmington Hills, MI, United States of America

Akshay Gokhale