Toyota Research Institute, Inc.

Jin Ge

Miroslav Baric

Timothee J Cazenave

Blake Warren Wulfe

Jiachen Li

Systems and methods for trajectory planning for an autonomous vehicle, may include: computing features for each of the plurality of candidate trajectories; computing scores for the features of the candidate trajectories, wherein the scores are based on parameter values associated with their corresponding final trajectories; determining, based on the computed scores, a trajectory of the candidate trajectories to be used as a warm-start trajectory for trajectory optimization and applying the warm-start trajectory to develop a final trajectory for the vehicle; and autonomously operating the autonomous vehicle in accordance with the final trajectory.

Data-driven warm start selection for optimization-based trajectory planning

System, methods, and other embodiments described herein relate to lane keeping in a vehicle. In one embodiment, a method includes determining lane boundaries according to at least the sensor data and a map. The method includes defining a reference system over a lane defined by the lane boundaries. The method includes evaluating vehicle boundary points within the reference system as a cost in optimizing a trajectory of the vehicle and using an R-function that defines geometric relationships between the vehicle boundary points and the reference system. The method includes providing an indicator about the trajectory to control the vehicle.

Systems and methods for using R-functions and semi-analytic geometry for lane keeping in trajectory planning

Systems and methods are provided for implementing hybrid prediction. Hybrid prediction integrates two deep learning based trajectory prediction approaches: grid-based approaches and graph-based approaches. Hybrid prediction techniques can achieve enhanced performance by combining the grid and graph approaches in a manner that incorporates appropriate inductive biases for different elements of a high-dimensional space. A hybrid prediction framework processor can generate trajectory predictions relating to movement of agents in a surrounding environment based on a prediction model generating using hybrid prediction. Trajectory predictions output from the hybrid prediction framework processor can be used to control an autonomous vehicle. For example, the autonomous vehicle can perform safety-aware and autonomous operations to avoid oncoming objects, based on the trajectory predictions.

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Santa Clara, CA, United States of America

Jin Ge