Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota

John Michael McNew

Vladimeros Vladimerou

Derek Stanley Caveney

Rohit Pandita

David A Hamilton

A least restrictive allowable driving state of a semi-autonomous driving system is determined based on one or more threats and sensor performance. A current driving state and a future driving state are determined based on an attention state and a steering state of a driver. Warnings are provided to the driver in order to match the current driving state to the future driving state. Driver interaction and attention are enforced when the driver does not respond to the warnings.

Management of driver and vehicle modes for semi-autonomous driving systems

A method and apparatus to predict the most probable path of a vehicle along a roadway which may contain a junction splitting the roadway into a rootpath and one or more sub-paths, each containing one or more lanes. The method and apparatus also process vehicle sensor information pertaining to driver corrections, road boundary detection, lane marker detection, map information availability, and road surface quality at the current position of the vehicle as well as from previous passes of the vehicle past the same location and other vehicles passing the same location to provide a warning to the driver to retake control of the vehicle if information used by the automatic vehicle control will become unavailable a predetermined travel distance ahead of the current position of the vehicle.

Method and apparatus for predicting most probable path of vehicle travel and vehicle control loss preview

A controller for predicting cruising speeds of a vehicle includes a processor and an extracting unit to extract feature data from segments of a prior trajectory of the vehicle, the feature data including cruising speeds of the vehicle and predictive feature data. The controller also includes a model generator to generate a probabilistic model associating the predictive feature data with the cruising speeds of the vehicle and a predicting unit to predict a cruising speed of the vehicle for a target segment, which is an upcoming cruising segment of the vehicle, by conditioning the probabilistic model on real-time predictive feature data of segments of a current trajectory.

Prediction of driver-specific cruise speed using dynamic modeling

Systems and methods for estimating local traffic flow are described. One embodiment of a method includes determining a driving habit of a user from historical data, determining a current location of a vehicle that the user is driving, and determining a current driving condition for the vehicle. Some embodiments include predicting a desired driving condition from the driving habit and the current location, comparing the desired driving condition with the current driving condition to determine a traffic congestion level, and sending a signal that indicates the traffic congestion level.

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Ann Arbor, MI, United States of America

John Michael McNew