Toyota Jidosha Kabushiki Kaisha

Toyota Motor Engineering & Manufacturing North America, Inc.

The University of Newcastle Upon Tyne

Bardh Hoxha

Danil V Prokhorov

Georgios Fainekos

Tomoya Yamaguchi

Abdelrahman Hekal

Jacob W Anderson

Mitchell Black

Sergiy Bogomolov

Hideki Okamoto

System, methods, and other embodiments described herein relate to implementing risk-bounded control barrier functions. In one embodiment, a method includes estimating a state of a vehicle; planning a trajectory based on the state of the vehicle; determining a nominal control input based on the trajectory; applying a risk-bounded control barrier function to determine a control input; and applying the control input to the vehicle.

Systems and methods for risk-bounded control barrier functions

Systems and methods are provided that implement spatio-temporal query of perception data streams. A system is distinctly designed to implement efficient and flexible spatio-temporal queries, which conduct pattern matching over perception data streams for automotive applications, particularly autonomous vehicles. The system is also designed to enable the spatio-temporal queries to be expressed in SpREs (Spatial Regular Expressions), which is implemented as a querying language that combines the ease of REs (regular expressions) with the enhanced capabilities of spatial logic. Additionally, a method includes receiving a command associated with a spatio-temporal query, wherein the expression comprises spatial regular expressions (SpREs). Thereafter, performing the spatio-temporal query of a plurality of perception data streams using the SpREs, where the SpREs describe spatio-temporal patterns to be searched within the plurality of perception data streams.

Methods and systems for spatio-temporal regular expression matching

Systems, methods, and other embodiments described herein relate to estimating reachable states for a vehicle using a neural model. In one embodiment, a method includes generating an unverified command by a first controller and a safety command by a second controller for a projected path by an automated driving system (ADS) associated with a vehicle. The method also includes estimating reachable states by a neural network (NN) model that uses the unverified command and an initial condition for the vehicle, the reachable states representing positions and orientations for the vehicle within a bounded time and a state space associated with the initial condition and the projected path. The method also includes, upon determining that the reachable states satisfy collision criteria, executing by the vehicle a maneuver with the safety command for the projected path and generating subsequent commands using vehicle data from the first controller and the second controller.

Systems and methods for online monitoring using a neural model by an automated vehicle

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Canton, MI, United States of America

Bardh Hoxha