Toyota Jidosha Kabushiki Kaisha

Toyota Motor Engineering & Manufacturing North America, Inc.

Amr Abdelraouf

Kyungtae Han

Rohit Gupta

Hazem Abdelkawy

Systems and methods are provided for learning a driving behavior during a cut-in cut-out event or other triggering event to create a profile that adjusts operation of the adaptive cruise control (ACC) component of the vehicle to mimic the preferences of the driver. The profile may be based on data collected during a previous cut-in cut-out event or other triggering event. The data may be transmitted to an adaptive cruise control system that uses the data as input to a machine learning model. Output of the machine learning model may update the profile for the driver that operates the vehicle in ACC during the cut-in cut-out event. When the vehicle is operating in ACC and an event is within a threshold value of the cut-in cut-out event occurs at a later time, the vehicle may apply rules defined in the profile.

Trigger event personalized adaptive cruise control (P-ACC)

An example operation includes one or more of receiving position data of an ego vehicle and of one or more surrounding vehicles of the ego vehicle via sensors of the ego vehicle while the ego vehicle and the surrounding vehicles are travelling along a road, predicting, via a machine learning model, a trajectory of the ego vehicle and trajectories of the one or more surrounding vehicles based on the position data, generating a routing instruction for the ego vehicle based on the predicted trajectory of the ego vehicle and the predicted trajectories of the one or more surrounding vehicles, and transmitting the routing instruction to a display associated with the ego vehicle.

Lane change assist for inexperienced driver

Embodiments of methods and systems include determining a driving maneuver to be performed by an ego vehicle based on a road geometry and a lane geometry, the ego vehicle surrounded by one or more surrounding vehicles, retrieving a trajectory model corresponding to the driving maneuver, encoding kinematic data of the surrounding vehicles relative to the ego vehicle to generate spatial relationships between the surrounding vehicles and the ego vehicle, predicting future trajectories of the surrounding vehicles based on the spatial relationships, the kinematic data, the road geometry, and the lane geometry, generating a collision coefficient based on the future trajectories and the driving maneuver, and when the collision coefficient is greater than a predetermined threshold value, determining an escape route for the ego vehicle, and causing the ego vehicle to autonomously operate based on the escape route.

Methods and systems for collision control of moving vehicles surrounded by vehicles

An example operation includes one or more of receiving driving data of a first vehicle, receiving driving data of a second vehicle driving in a different lane than the first vehicle, determining that the first vehicle and the second vehicle will collide via execution of a machine learning model on the driving data of the first and second vehicles, and displaying a warning on a user interface associated with one or more of the first and second vehicles.

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Foster City, CA, United States of America

Amr Abdelraouf