Hitachi, Ltd.

Hitachi Astemo Ltd.

Yashodeep Dilip Lonari

Subrata Kumar Kundu

Naveen Kumar Bangalore Ramaiah

Akshay Gokhale

Xiaoliang Zhu

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 determine a plurality of candidate routes between a source location of a vehicle and a destination location for the vehicle. Further, the system may determine one or more respective observation zone volumes for each candidate route of the plurality of candidate routes. The system may determine a field of view (FOV) for one or more vehicle sensors onboard the vehicle. In addition, the system may select a route for the vehicle from the plurality of candidate routes based at least on comparing an overlap of the FOV with the respective observation zone volumes for the candidate routes.

Precautionary observation zone for vehicle routing

In some examples, a system may determine a plurality of routes between a source location and a destination location. Further, the system may segment each route of the plurality of routes into multiple road segments. The system may determine a first field of view (FOV) for each road segment. In addition, the system may receive vehicle sensor configuration information for vehicle sensors on board a vehicle. The system may determine a second FOV for the vehicle sensors. Additionally, the system may select a route for the vehicle based at least on comparing the second FOV with the first FOV for a plurality of the road segments.

Vehicle routing using connected data analytics platform

Systems and methods described herein are directed to predicting velocity and trajectory of a vehicle based on a route to be navigated for a vehicle, thereby estimating the vehicle drive cycle, for one or more segments of the route. The predicted velocity trajectory is determined from a machine learning function that takes into account historical information from vehicle routes traveled by other vehicles.

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Novi, MI, United States of America

Yashodeep Dilip Lonari